Assessing the Vulnerability of Nomadic Pastoralists’ Livelihoods to Climate Change in the Zhetysu Region of Kazakhstan

Abstract

Kazakhstan is historically a livestock-producing country. For the first time in this study, we attempted to assess the vulnerability of nomadic pastoralists in Kazakhstan to climate change using the Livelihood Vulnerability Index (LVI). To collect data, a survey of 100 household heads was conducted on fourteen main components and fifty-six sub-components. The study was conducted in the period from May to July 2022 in the Panfilov (PD) and Kerbulak (KD) districts of the Zhetysu region, where the Altyn-Emel State National Nature Park is located. The results of the study were combined using a composite index method and comparing different vulnerability indicators. Natural disasters, which manifest as the effects of drought, temperature fluctuations, and precipitation, contribute most to the vulnerability of nomads living in remote mountain areas with a complex infrastructure. According to the results of the study, nomads of both regions have high vulnerability in such components as natural resources, human–wildlife conflict, housing type, agriculture and food security, and social networks. High vulnerability in the “Finances and incomes” component was found only in the pastoralists of the PD. Identifying the levels of vulnerability of nomadic households to climate change, as well as understanding their adaptation strategies, will enable pastoralists to gain access to new ways of reducing the vulnerability of their livelihoods. Currently, the country practices a strategy to reduce the vulnerability of pastoral nomads’ livelihoods by insuring livestock against natural or natural hazards and other risks; involving the population in environmental-protection activities and helping them to obtain sustainable financial resources when they refuse to hunt endangered animals; non-agricultural diversification of high-altitude ecotourism in rural areas in their area of residence; and improving financial literacy by providing training and providing information on low-interest loans under state projects and livestock subsidy mechanisms, as well as training in organizing cooperatives within the framework of legal status, which will ensure them stable sales of products and income growth. The results of software research serve as a basis for taking measures within the framework of the development and implementation of state programs for climate change adaptation of the Environmental Code of the Republic of Kazakhstan, where agriculture is one of the priority areas of management.

1. Introduction

According to the Intergovernmental Panel on Climate Change (IPCC), climate change is defined as “…a change in the state of the climate that can be identified by changes in its mean properties and/or variability, and that persists for an extended period” [1]. In August 2021, the IPCC published its Sixth Assessment Report, marking a significant milestone in scientific research by unequivocally attributing the accelerated pace of climate change to anthropogenic activities [2]. This acceleration is expected to result in more frequent and severe natural disasters, accompanied by emerging threats such as rising sea levels, glaciers melting, and diminishing water resources [1]. Current climatic shocks and pressures have already exerted a substantial impact on the vulnerability of households, especially those in rural areas [3].

Currently, effective approaches to enhancing livelihood resilience include establishing a long-term risk early warning mechanism, risk management, promoting livelihood diversification, and implementing proactive environmental policies [4].

Kazakhstan has ratified over 30 international conventions related to environmental protection. Following the Sendai Framework, the Ministry of Emergency Situations of Kazakhstan, in collaboration with the United Nations Office for Disaster Risk Reduction, is developing the Desinventar Sendai database to track losses from natural disasters. Nevertheless, Kazakhstan requires assistance in integrating information obtained through space technologies in real-time into the operations of forecasting and emergency response services [5].

Diversification of livelihoods is crucial for household survival [6]. According to research conducted in South Asia, farmers tend to diversify their income sources more actively when natural disasters occur more frequently [7].

Southern Kazakhstan, due to its geographical location, receives direct solar radiation for the majority of daylight hours, accounting for 83–96% of the maximum possible value [8]. According to the International Energy Agency (IEA), solar energy can meet approximately 20–25% of the country’s energy needs over a span of 40 years. Furthermore, this share of required electricity can reduce carbon dioxide emissions by 6 billion tons annually [9]. Kazakhstan is advancing solar energy technologies, specifically the production of photovoltaic modules using locally sourced silicon. With the support of the government and various international agencies, Kazakhstan is taking steps towards the development of a renewable energy sector. In alignment with Kazakhstan’s transition to a “green economy” concept, the government has set ambitious goals: achieving a 3% share of renewable energy sources in the country’s total energy balance by 2020, 10% by 2030, and 50% by 2050. As part of government support for entrepreneurs, the Ministry of Agriculture of Kazakhstan subsidizes up to 50% of the cost of purchased solar panels [10], a practice actively adopted by pastoralists in grazing areas.

Vulnerability to climate change can be reduced through non-agricultural diversification [11]. The insurance system in Kazakhstan’s agro-industrial complex is being improved and is attracting increasing attention from the government. The insurance mechanism introduced in 2020 is voluntary and covers both crop farming and animal husbandry. Currently, the subsidy for insurance premiums has been increased from 50% to 80%.

Together with farmers from the Kerbulak district of the Zhetysu region of Kazakhstan, approximately 40 households from across the republic participated in the livestock insurance program, aimed at protection from natural disasters, infectious diseases, and other risks. Farmers, recognizing the benefits of insurance products, are increasingly turning to the insurance system. In 2020–2021, agricultural producers entered into 254 insurance contracts, covering over 265,700 hectares of land and 2.6 million heads of livestock. In the first 7 months of this year, 213 contracts have been concluded, covering 354,000 hectares and over 114,000 heads of livestock [12].

Ecological vulnerability denotes a sensitive response and the ability of ecosystems to self-recover concerning external influences within a specific scope of time and space. Associated research explores this impact, sensitivity, and adaptability [13]. Although ecological vulnerability is widely discussed, it lacks a precise definition; nevertheless, it can be measured [14]. The assessment of the current situation is the most extensively studied aspect of measuring ecological vulnerability, and the most commonly used method is index assessment [15]. One way to better understand these complex relationships is by assessing the vulnerability of livelihoods. LVI research can provide policymakers and managers with empirical guidance for developing programs and actions to reduce vulnerability and formulate climate change adaptation plans [16].

In this study, the Livelihood Vulnerability Index (LVI) was used for the first time to identify indicators of the adaptive capacity of nomadic households in the Zhetysu region living in high-altitude areas bordering the Altyn-Emel State National Park. The research findings will serve as a basis for taking measures within the framework of developing and implementing state programs for climate change-adaptation in accordance with the Environmental Code of Kazakhstan. These measures include livestock insurance against natural or environmental hazards and other risks, involving indigenous populations in conservation activities, enhancing the financial literacy of livestock breeders through livestock farming subsidies, and diversification beyond agriculture.

2. Materials and Methods

2.1. Research Area

This study was carried out in two districts of the Zhetysu region in Kazakhstan: Panfilov and Kerbulak, where Altyn-Emel National Park (NP) is located (Figure 1). Zhetysu region, with its center in Taldy-Kurgan town, was formed in 2022. Panfilov district, with its center in Zharkent town, was formed in 1928, Kerbulak district (with its center in Saryozek’s urban-type settlement) in 1973 [17].

Altyn-Emel National Park, with an area of 307,653.35 thousand hectares, was established in April 1996 in order to preserve rare, endangered, and especially valuable species of flora and fauna. The territory includes both mountainous and desert-clay-rubble landscape complexes, and consists of both a flat part adjoining the right bank of the Ili River, and a mountainous part with spurs of the Zhetysu Alatau mounts. The Red Book of Kazakhstan lists 21 plant species, and 56 animal species, including snow leopards, argali, kulans and goitered gazelles [18]. Panfilov and Kerbulak districts are characterized by an agro-cattle-breeding and livestock-breeding orientation to the economy.

Data Sampling and Collecting

This study is supplemented by the name of the method, which is highlighted in green. The study is based on the primary data of an expedition collected from 25 May to 28 June 2022, using interviews to obtain information about the nomads’ livelihood vulnerability. The LVI survey in Kazakh and Russian languages, with the participation of the heads of households, was carried out by the pre-prepared field personnel of the expedition after receiving special permission from the State Department of Natural Resources regulating the issues of the ecology of Zhetysu region. The heads and members of the households were informed about the purpose of the study before the survey started. When selecting households in both districts, we used the sampling procedure from the “random walk” methodology of the expanded immunization program of the World Health Organization (WHO) [19] with additions by M. Khan et al. (2009) [20]. A detailed list of the main components and subcomponents that we used in the study is provided in

Table 1. Classification of the principal components for Calculation of LVI–IPCC.

IPCC Factors and Principal Components
Exposure	Human—wildlife conflict Natural catastrophes and climate change
Adaptive capacity	Livelihood strategies Natural resources Social networks Energy Infrastructure Sociodemographic profile Land Finance and income
Sensitivity	Agriculture and food security Health Housing type Water resources and sanitation

. Climate data on daily minimum and maximum temperatures, as well as precipitation for 2017–2022, were analyzed according to the snowmobile routes of the Branch of the Kazmeteoservice of Zhetysu region. When selecting households in both districts, we used the sampling procedure from the “random walk” methodology of the expanded immunization program of the World Health Organization (WHO) [19] with additions by M. Hahn et al. (2009) [20].

2.2. Methods

The magnitude of external threats to the people’s well-being, as well as their households and communities, determines the degree of vulnerability [21]. According to the IPCC Third Assessment Report, Sujakhu data “…vulnerability to climate change is a function of exposure, sensitivity and adaptive capacity” [6,22]. Following the LVI and LVI–IPCC indices developed by Hahn et al. (2009), this study revealed the nomads’ vulnerability in terms of livelihoods [20].

2.2.1. Calculation of the Livelihood Vulnerability Index (LVI)

The LVI calculation uses the method developed by Hahn et al. (2009) [20], with an analysis of six principal components, like sociodemographic profile, livelihood strategies, social networks, natural catastrophes and climate change, and health and water resources (Table 1). A literature review on each principal component, as well as the collection of data from household testing in Bhutan in 2021–2022, allowed Dr. Sonam Wangyel Wang to supplement the principal components such as land (L), agriculture and food security (AFS), finance and income (FI), human–wildlife conflict (HWC), infrastructure (I), natural resources (NR), housing type (HT), energy (E), water resources and sanitation (WSS), (Table 1). Thus, Table 1 provides data for 56 subcomponents and 14 principal components.

The LVI calculation process in this study consists of four main steps. To begin, 1 and 2 transform steps, from UNDP (2007) by Hahn et al. (2009), Rai P. et al. (2022), converted raw test data into measurable units such as percentages, ratios, and indices—“…some of the sub-components were measured on a different scale. It was necessary to standardize each one of them as an index developed using” [20,23,24]:

$${Index}_{S_d }=\frac{S_d-S_{min}}{S_{max}-S_{min}}$$

(1)

where, according to Hahn et al. (2009), Abd Majid et al. (2019) “…Sd is the model sub-component for district d, and Smin and Smax are the minimum and maximum values for every sub-component, respectively” [20,25].

Stage 3, according to the method of Hahn et al. (2009) [20], with averaging of each subcomponent and the subsequent calculation of the value of each principal component by Equation (2), is as follows:

$$M_d=\frac{{\sum }_{i=1}^n{S}_{di}}{n}$$

(2)

where, according to Hahn et al. (2009), Abd Majid et al. (2019), “…Md is one of the fourteen main components for district d, indexSdi is expressed as the sub-component of index i, and n is the number of sub-dimensions in a major dimension of Md” [20,25].

Stage 4 is calculated according to the method of Hahn et al. (2009), averaging the LVI at the district level using Equation (3) after calculating the value of each of the fourteen principal components for the district [20]. A detailed step-by-step procedure for calculating LVI for all fourteen principal components and their respective subcomponents for households in Kerbulak and Panfilov districts is presented in Appendix A,

Table A1. Calculating the principal components for the LVI for Kerbulak and Panfilov districts, Zhetysu region.

No	Principal Components	Subcomponents	Units	Kerbulak	Panfilov	Max Value in Both Districts	Min Value in Both Districts	Index Value for KD	Index Value for PD	Principal Component Values for KD	Principal Component Values for PD
1	Sociodemographic profile	Demographic dependence coefficient	Ratio	0.58	0.7	-	-	0.58	0.7	0.545	0.602
		Household heads (HH) who graduated from high school	Percent	71.63	85.17	100	0	0.716	1
		Female-headed hh	Percent	4.5	10.5	100	0	0.045	0.11
		HH heads average age	Years	52.31	49.73	53	32	0.967	0.84
		Female-headed HH average age	Years	45.38	47.21	57	37	0.419	0.51
2	Social networks	HHs received information on climate change and natural disasters	Percent	100	100	100	0	1	1	0.680	0.673
		HHs supported by the State	Percent	61.27	54.91	100	0	0.613	0.549
		HHs who are members of any community groups	Percent	56.93	63.51	100	0	0.569	0.635
		HH who have a radio at home	Percent	21.85	18.32	100	0	0.219	0.183
		HH who have a mobile phone	Percent	100	100	100	0	1	1
3	Health	Average time to get to the closest medical service center	Minutes	62.15	75.33	75	15	0.786	1.006	0.195	0.262
		Average time to get to the closest medical service center from the pasture	Minutes	180.45	248.61	420	60	0.335	0.524
		HHs reported TB deaths	Percent	0	1	100	0	0	0.01
		HHs reported infant death	Percent	5	3	100	0	0.05	0.03
		HHs reported HIV deaths	Percent	0	0	100	0	0	0
		HHs reported a death report on maternal death	Percent	0	0	100	0	0	0
4	Agriculture and food security	HHs dependent on raising livestock for food production	Percent	95.5	95.5	100	0	0.955	0.955	0.694	0.756
		HHs engaged in agriculture and grazing	Percent	32.91	27.84	100	0	0.329	0.138
		HHs that have enough food	Percent	100	100	100	0	1	1
		Average livestock units owned by HHs	Count	39.25	63.31	100	0	0.393	0.534
5	Livelihood strategies	HHs whose members work outside the community	Percent	65.51	73.18	100	0	0.655	0.732	0.277	0.495
		HHs with members working in civil service	Percent	0	0	100	0	0	0
		HHs whose members are engaged in transport service (taxi drivers, truckers, buses, etc.)	Percent	17.63	75.26	100	0	0.176	0.753
6	Human-wildlife conflict	HHs reported livestock deaths due to snow leopard	Percent	89.57	91.32	100	0	0.426	0.353	0.749	0.710
		HHs reported loss of livestock due to other predators (bears and wolves)	Percent	35.04	21.63	100	0	0.350	0.216
		HHs reported livestock losses due to climate and weather	Percent	100	100	100	0	1	1
7	Finance and income	HHs that have debts to neighbors, relatives or financial institutions	Percent	100	100	100	0	1	1	0.677	0.738
		HHs with no access to loans	Percent	35.43	47.61	100	0	0.354	0.476
8	Infrastructure	Average time to get to the closest vehicle parking lots	Minutes	27.45	15.38	70	20	0.149	−0.092	0.433	0.674
		Average time to get to the closest vehicle parking lot from the pasture	Minutes	360.18	380.72	360	180	1.001	1.115
		Average time to get to the market	Minutes	70.15	360.18	360	20	0.148	1
9	Housing type	HHs living in permanent houses (mortgag)	Percent	74.57	81.92	100	0	0.746	0.819	0.873	0.910
		HHs living in yurts, semi-permanent houses and pasture camps	Percent	100	100	100	0	1	1
10	Natural resources	HHs dependent on natural resources	Percent	100	100	100	0	1	1	1	1
11	Energy	HHs using only firewood for cooking	Percent	1.3	0.5	100	0	0.013	0.005	0.571	0.545
		HHs using livestock manure for cooking	Percent	0.7	0.5	100	0	0.007	0.005
		HHs using solar panels	Percent	100	100	100	0	1	1
		HHs using gas cylinders for cooking	Percent	100	100	100	0	1	1
		HHs using coal for cooking and heating	Percent	83.41	71.56	100	0	0.834	0.716
12	Water resources and sanitation	HHs having access to clean drinking water	Percent	83.25	87.73	100	0	0.833	0.877	0.400	0.400
		HHs with no access to safe drinking water	Percent	16.75	12.27	100	0	0.168	0.123
		HHs reporting conflicts over water	Percent	0	0	100	0	0	0
		HHs with access to private toilets	Percent	100	100	100	0	1	1
		HHs reporting a shortage of drinking water for livestock	Percent	0	0	100	0	0	0
13	Land	HHs owning pastures	Percent	37.64	43.25	100	0	0.376	0.433	0.632	0.620
		HHs reporting a shortage of pastures	Percent	27.41	25.71	100	0	0.274	0.257
		HHs reporting degradation of pasture lands due to weather and climatic conditions during the last 10 years	Percent	100	100	100	0	1	1
		HHs that believe that weather/climate change is the cause of pasture degradation	Percent	87.65	79.13	100	0	0.877	0.791
14	Natural catastrophes and climate change	HHs observing or experiencing natural hazards due to drought over the past 5 years	Percent	100	100	100	0	1	1	0.522	0.521
		HHs observing or experiencing natural hazards due to hail in the last 5 years	Percent	100	100	100	0	1	1
		HHthat received an advance climate warning	Percent	100	100	100	0	1	1
		HHs receiving compensation from relevant agencies for loss and damage to livestock assets due to natural disasters related to climate change	Percent	0	0	100	0	0	0
		Average number of livestock lost due to climate	Percent	5.43	7.15	100	0	0.054	0.072
		Mean standard deviation of mean monthly temperature: maximum temperature SDtsmax	Celsius	1.49	1.56	5.95	−2.98	0.501	0.508
		Mean standard deviation of monthly mean minimum temperature SDtsmin	Celsius	−6.48	−6.98	0.99	−14.88	0.529	0.498
		Mean standard deviation of monthly rainfall SDosm	mm	1.24	1.18	13.39	0	0.093	0.088
Overall LVI										0.538	0.576

.

$${LVI}_d=\frac{{\sum }_{i=1}^7{W}_{Mi}{M}_{di}}{{\sum }_{i=1}^7{W}_{Mi}}$$

(3)

Once the values for each of the fourteen principal components for KD and PD were calculated according to the method of Hahn et al. (2009), Rai et al. (2022), “…they were averaged using Equation (3) to obtain a specific LVI for the district” [20,24].

Once the values for each of the fourteen principal components for the two study districts were calculated according to the method of Hahn et al. (2009), Rai et al. (2022), “…they were averaged using Equation (3) to obtain the specific district-wise LVI” [20,24].

In calculations according to Equation (3), we performed a control calculation on equation 4 according to the method of Suillivan et al. (2002), Rai et al. (2022) [24,26]:

$${LVI}_d=\frac{W_{SDP}{SDP}_d+W_{LS}{LS}_d+W_{SN}{SN}_d+W_H{H}_d+W_F{F}_d+W_W{W}_d+W_{NDCV}{NDCV}_d}{W_{SDP}+W_{LS}+W_H+W_{SN}+W_F+W_W+W_{NDCV}}$$

(4)

where according to Suillivan et al. (2002), Rai P. et al. (2022) “…LVId is the Livelihood Vulnerability Index for district d, which equals the weighted average of the seven major components for the respective study district”. The weights of each principal component contribute equally to the overall LVI, WMi determined according to the method of Suillivan et al. (2002), Rai P. et al. (2022) “…are the number of sub-components that make up each major component and are included to ensure that all sub-components contribute equally to the overall LVI. For this study, LVI was scaled from 0 (least vulnerable) to 0.5 (highly vulnerable)” [24,26].

2.2.2. Calculation of LVI–IPCC

The LVI–IPCC calculation method (2007) [6] has been refined by Hahn et al. (2009), considering the definition of vulnerability [20] within the framework of factors such as exposure, sensitivity and adaptive capacity (Table 1). Exposure in KD and PD consists of components such as human—wildlife conflict, natural catastrophes and climate change. Adaptive capacity is quantified by components such as energy, livelihood strategies, natural resources, social networks, sociodemographic profile, infrastructure, finance and income, and land. Sensitivity is determined using components such as water resources and sanitation, housing type, health, agriculture and food security [27]. The combination of the principal components in the calculation of LVI–IPCC occurs by a categorization scheme corresponding to the data in Table 1, in accordance with Equation (5):

$${CF}_d=\frac{{\sum }_{i=1}^n{W}_{Mi}{M}_{di}}{{\sum }_{i=1}^n{W}_{Mi}}$$

(5)

where CFd defined by IPCC (2007), Hanh et al. (2009), Abd Majid et al. (2019) “…represents the contribution factor (exposure, sensitivity, or adaptive capacity) as defined by the IPCC for district d, WMi is the weightage for every major component, Mdi is comprised of the major components for district d, indexed by i, and n individually represents the number of major components in their contribution factors. Once exposure, sensitivity, and adaptive capacity were calculated, the three contributing factors were combined using Equation (6)” [6,20,25]:

$$\mathrm{L}\mathrm{V}\mathrm{I}-{\mathrm{I}\mathrm{P}\mathrm{C}\mathrm{C}}_d=(e_d+a_d)\ast S_d$$

(6)

where LVI–IPCCd, according to IPCC (2007), Hanh et al. (2009), Abd Majid et al. (2019) “….is the LVI for district d expressed using the IPCC vulnerability framework, ed is the exposure score calculated for district d (corresponding to the Natural Disaster and Climate Variability major component), ad is the calculated adaptive capacity score for district d (weighted average for the Sociodemographic, Social Networks, and Livelihood Strategies major components), and S is the sensitivity score calculated for district d (weighted average of Health, Food, Water, Land, Housing, and Finance and Income major components). The LVI-IPCC is scaled from −1 (least vulnerable) to +1 (most vulnerable)” [6,25,28,29].

3. Results

3.1. LVI

LVI data for 14 components, and 56 subcomponents, for the Kerbulak district (hereinafter KD) and Panfilov district (hereinafter PD) are shown in Figure 2,

Table 2. Indexed subcomponents, principal components and a common LVI for the Kerbulak and Panfilov districts, Zhetysu region.

No.	Principal Components	Sub-Components	Units	Kerbulak	Panfilov
1	Sociodemographic profile	Demographic dependece coefficient	Ratio	0.58	0.7
		Household heads (HH) who graduated from high school	Percent	71.63	85.17
		Female-headed HH	Percent	4.5	10.5
		HH heads average age	Years	52.31	49.73
		Female-headed HH average age	Years	45.38	47.21
		Total:		0.545	0.602
2	Social networks	HHs received information on climate change and natural disasters	Percent	100	100
		HHs supported by the State	Percent	61.27	54.91
		HHs who are members of any community groups	Percent	56.93	63.51
		HH who have a radio at home	Percent	21.85	18.32
		HH who have a mobile phone	Percent	100	100
		Total:		0.680	0.673
3	Health	Average time to get to the closest medical service center	Minutes	62.15	75.33
		Average time to get to the closest medical service center from the pasture	Minutes	180.45	248.61
		HHs reported TB deaths	Percent	0	1
		HHs reported infant death	Percent	5	3
		HHs reported HIV deaths	Percent	0	0
		HHs reported a death report on maternal death	Percent	0	0
		Total:		0.195	0.262
4	Agriculture and food security	HHs dependent on raising livestock for food production	Percent	95.5	95.5
		HHs engaged in agriculture and grazing	Percent	32.91	27.84
		HHs that have enough food	Percent	100	100
		Average livestock units owned by HHs	Count	39.25	63.31
		Total:		0.694	0.756
5	Livelihood strategies	HHs whose members work outside the community	Percent	65.51	73.18
		HHs with members working in civil service	Percent	0	0
		HHs whose members are engaged in transport service (taxi drivers, truckers, buses, etc.)	Percent	17.63	75.26
		Total:		0.277	0.495
6	HW conflicts	HHs reported livestock deaths due to snow leopard	Percent	89.57	91.32
		HHs reported loss of livestock due to other predators (bears and wolves)	Percent	35.04	21.63
		HHs reported livestock losses due to climate and weather	Percent	100	100
		Total:		0.749	0.710
7	Finance and income	HHs that have debts to neighbors, relatives or financial institutions	Percent	100	100
		HHs with no access to loans	Percent	35.43	47.61
		Total:		0.677	0.738
8	Infrastructure	Average time to get to the closest vehicle parking lots	Minutes	27.45	15.38
		Average time to get to the closest vehicle parking lot from the pasture	Minutes	360.18	380.72
		Average time to get to the market	Minutes	70.15	360.18
		Total:		0.433	0.674
9	Housing type	HHs living in permanent houses (mortgage)	Percent	74.57	81.92
		HHs living in yurts, semi-permanent houses and pasture camps	Percent	100	100
		Total:		0.873	0.910
10	Natural resources	HHs dependent on natural resources	Percent	100	100
		Total:		1	1
11	Energy	HHs using only firewood for cooking	Percent	1.3	0.5
		HHs using livestock manure for cooking	Percent	0.7	0.5
		HHs using solar panels	Percent	100	100
		HHs using gas cylinders for cooking	Percent	100	100
		HHs using coal for cooking and heating	Percent	83.41	71.56
		Total:		0.571	0.545
12	Water resources and sanitation	HHs having access to clean drinking water	Percent	83.25	87.73
		HHs with no access to safe drinking water	Percent	16.75	12.27
		HHs reporting conflicts over water	Percent	0	0
		HHs with access to private toilets	Percent	100	100
		HHs reporting a shortage of drinking water for livestock	Percent	0	0
		Total:		0.4	0.4
13	Land	HHs owning pastures	Percent	37.64	43.25
		HHs reporting a shortage of pastures	Percent	27.41	25.71
		HHs reporting degradation of pasture lands due to weather and climatic conditions during the last 10 years	Percent	100	100
		HHs that believe that weather/climate change is the cause of pasture degradation	Percent	87.65	79.13
		Total:		0.632	0.620
14	Natural catastrophes and climate change	HHs observing or experiencing natural hazards due to drought over the past 5 years	Percent	100	100
		HHs observing or experiencing natural hazards due to hail in the last 5 years	Percent	100	100
		HH that received an advance climate warning	Percent	100	100
		HHs receiving compensation from relevant agencies for loss and damage to livestock assets due to natural disasters related to climate change	Percent	0	0
		Average number of livestock lost due to climate	Percent	5.43	7.15
		Mean standard deviation of mean monthly temperature: maximum temperature SDtsmax	Celsius	1.49	1.56
		Mean standard deviation of monthly mean minimum temperature SDtsmin	Celsius	−6.48	−6.98
		Mean standard deviation of monthly rainfall SDosm	Mm	1.24	1.18
		Total:		0.522	0.521
Overall LVI				0.538	0.576

, Appendix A.

When assessing climate variability, we took into account TmaxM, TminM—the maximum, minimum monthly temperature, TsM—the average monthly temperature, SDtsm—the average standard deviation of the average monthly temperature, SDtsmax—the average standard deviation of the average monthly maximum temperature, SDtsmin—the average standard deviation of the average monthly average minimum temperature, SDosm—the average monthly precipitation, as well as SDsm—the average monthly amount precipitation over the last 5 years. Extremely high variability in precipitation and temperature over the period under study resulted in a high LVI score. It should be noted that the Zhetysu region is located in the south-eastern part of Kazakhstan. Pastures in the highlands of the Zhetysu Alatau in temperate latitudes determined the average vulnerability index of the climate change component and is 0.522 for the KD, and 0.521 for the PD (Table 2). The SDTmax index of the average monthly value is 0.501 in the KD, and 0.508 in the PD. The indicator SDTmin of the average monthly value is 0.529 in the KD and 0.498 in the PD. The SD indicator of monthly precipitation is 0.093 for the KD and 0.088 for the PD (Appendix A, Table A1).

Indicators with high vulnerability for nomads in the KD and PD were identified for such components as natural resources, housing type, human—wildlife conflict, agriculture and food security, finance and income (only in the PD), and social networks. Natural resources scored the highest in both districts, with a score of 1.0. Housing type is the second principal component with a high index—0.873 in the KD, and 0.910 in the PD. The conflict of human—wildlife, is the third principal component with a high index—0.749 in the KD, and 0.710 in the PD. Agriculture and food security is the fourth principal component with a high index—0.694 in the KD, and 0.756 in the PD. Finance and income are the fifth principal component, with an extremely average index of 0.677 in the KD, and a high index of 0.738 in the PD. Social networks are the sixth principal component with a high index—0.680 for the KD and 0.673 for the PD.

Land is the seventh principal component with an extremely average index—0.632 for the KD and an extremely average index of 0.620 for the PD. HHs who believe that climate change is the cause of pasture degradation is 87.65% in the KD, and 79.13% in the PD. Pastoral ecosystems are highly vulnerable to external impacts and under strong pressure in countries where pastoralism remains the main livelihood strategy. Pasture degradation in Central Asia due to overgrazing is often associated with pastoralists’ low skills and knowledge, overstocking and weak pasture management institutions as well [28,29]. Since the beginning of 2020, as a result of a detailed analysis of unused pasture lands, it has been established that there is a shortage of pasture for the population in the Zhetysu region. A total of 27.41% of HHs in the KD and 25.71% in the PD reported a shortage of pasture. The need for pasture in rural districts is planned to be met through their redistribution in accordance with “On Pastures” law [30].

Sociodemographic profile is the eighth principal component, with an average index of 0.545 for the KD, and 0.602 for the PD. The coefficient of demographic dependence is in a range from 0.58 for the KD to 0.70 for the PD. The number of the disabled population is more than 50% of the economically active population of the region, which indicates stagnation and low adaptive ability to the damage that may occur during natural hazards. The low value of the proportion of female headed HHs indicates low vulnerability and amounts to 10.5% in the the PD, and 4.5% in the KD. The level of education of HH heads indicates a high adaptive capacity and is 71.63% in the KD and 86.17% in the PD. According to the results of the sixth edition of the 2021 Sustainable Development Report by the members of the UN Sustainable Development Solutions Network, “…in Kazakhstan, indicators of the Quality Education goal are stagnating… the net enrollment ratio in primary education in 2019 is 86.9%, 2020—90.4%” [31].

Infrastructure is the ninth principal component with an extremely average index of 0.674 in the PD, and an average index of 0.433 in the KD. The time spent by the respondents in the study areas on traveling from the pasture to the nearest market indicates the population’s low adaptability and the difficulty in acquiring food and other necessary household goods. Longer times reflect lower adaptive ability to avoid risks in space [32]. The respondents travel along a serpentine winding dirt road from pastures to the market by private car within 70–360 min. All the markets in Panfilov district are located in the district center of Zharkent town. Residents can also reach the regional center in Taldykorgan town within more than 380 min.

Energy is the tenth principal component, with an average index of 0.571 for the KD and an average of 0.545 for the PD. A total of 100% of respondents use solar panels to generate electricity. As part of state support for entrepreneurs, the Ministry of Agriculture subsidizes up to 50% of the cost of purchased solar panels. The use of forest fuel causes serious damage to the environment [33]. It should be noted that the consumption of firewood from old, senile, dry trees on the pasture occurs with the permission of the representatives of the district forestry. Manure, as a natural energy resource, is the cheapest and most accessible source of natural energy resources for households [34]. However, only 0.5% of HHs in the PD and 0.7% in the KD use manure for cooking. In the PD, an automatic installation of gas supply was launched only for consumers of the district center. A total of 71.56% of the population in the PD, and 83.41% in the KD heat, their homes with coal. In particular, women face significant health and safety risks due to household air pollution and the need to carry heavy heating coal [35]. A total of 100% of respondents in the pasture use gas cylinders for cooking, as they are easy to use and do not require much labor.

Natural catastrophes and climate change are the eleventh principal component with an average index of 0.522 and 0.520 for the KD and PD, respectively. Natural fires in 2020–2021 are associated with an increase in air temperature by 1–2 degrees and there was a shortage of precipitation. Sarsenbaev et al., Iliev et al., proposed to study and introduce drought-resistant plants in connection with climate change [36,37].

Water resources and sanitation is the twelfth principal component with an average index of 0.4 in both districts. The “Blue Peace Central Asia” initiative (BPCA) aims to “support effective management of water resources considering the interplay of water, food, and energy from the local to regional levels, including managing climate change-related risks.” The Blue Peace Index assesses the management of shared water resources along five main dimensions: policy and legal framework, institutional mechanisms and participation, tools for managing water resources, infrastructure and financing, and cooperation. According to the “Blue Peace” index, Kazakhstan ranks 18th among 32 countries: 8th place (57.5 points) in the “Policy and Regulatory Framework” dimension, 12th place (61.0 points) in “Cooperation Context”, 15th place (45.9 points) in “Infrastructure and Financing”, 17th place (59.7 points) in “Institutions and Participation”, and 18th place (41.7 points) in the “Tools for managing water resources” dimension [38]. Higher value reflects greater vulnerability, and increases susceptibility to water-related diseases due to an inadequate drinking water supply [39]. According to Dzhabagieva et al., the volume of such works is very large and requires huge theoretical and experimental studies. In this regard, this work is only the beginning of a series of similar developments [40]. The natural and climatic conditions in the districts are favorable for a clean drinking water supply. In summer, households obtain 100% of their water from a spring on the pastures. The survey of respondents shows that the proportion of HHs that received information about natural disasters and the climate changing is 100%.

Livelihood strategies is the thirteenth low-index principal component of 0.277 for the KD, with an average index of 0.495 for the PD. A total of 7.63% of the HHs in the KD and 75.26% in the PD in winter–springtime are engaged in transportation service by private vehicles. The average livelihood diversification index is 0.33. HHs in both districts are engaged in agriculture, and animal husbandry, and do not use natural plant resources. In 2018–2022, respondents noted cases of bear attacks on people picking raspberries.

Health is the fourteenth low-index principal component of 0.195 in the KD, and with a low index of 0.262 for the PD. In the PD over the past three years, five infant deaths were registered, and, in the KD, three. A survey of HHs members in both districts showed that 97.54% of pastoralists are aware of the symptoms and ways of tuberculosis infection. HHs members from both districts state that it takes them an average of 62–76 min to travel by public transport from their permanent residence to a medical center. The country has developed a system of emergency medical care in cases of acute diseases that threaten human life. Things are different with medical care on pastures in summer and autumn. The survey results showed that it takes an average of 180–249 min by private car, 360–498 min by horse, to get from the pasture to the medical service center. The pastures are located high in the Zhetysu Alatau mountains and there is access to medical centers through a dirt serpentine road, and it becomes impassable in winter.

3.2. LVI–IPCC: Kerbulak versus Panfilov

The IPCC-LVI data for the Kerbulak district (hereinafter KD) and the Panfilov district (hereinafter PD) are shown in

Table 3. LVI–IPCC for Kerbulak and Panfilov districts, Zhetysu region (according to the IPCC methodology, 2001).

IPCC Contributing Factors	Kerbulak (KD)	Panfilov (PD)
Exposure	0.6	0.6
Adaptive capacity	0.6	0.7
Sensitivity	0.5	0.6
LVI-IPCC	0.018	−0.031

, Figure 2. The principal components according to the IPCC methodology are combined into a vulnerability triangle with the definitions of susceptibility, adaptive capacity and sensitivity [41]. The adaptive capacity index for the PD is 0.7, highly vulnerable, and for KD is 0.6, extremely average. The exposure rate in both districts is 0.6, extremely average. The sensitivity index varies and is 0.5 for the KD, and, for the PD, 0.6. The LVI–IPCC data of nomads for the PD is 0.031 and indicates low vulnerability. Data LVI–IPCC of nomads for KD is 0.018 (Table 3, Figure 3).

4. Discussion

There is no universally applicable methodological framework, nor is there a universal set of indicators, to measure the adaptation progress [42]. For the first time in Kazakhstan based on the LVI index by the method of Hahn [20], the nomads’ livelihood vulnerability in the Panfilov (hereinafter PD) and Kerbulak districts (hereinafter KD) of the Zhetysu region is identified. The 14-component and 56-subcomponent LVI is an alternative approach to analyzing the vulnerability of nomad households (hereinafter HH) to climate change.

Nomadic pasture climate variability, which manifests itself in the form of the impact of drought, as well as temperature and precipitation fluctuations, contributes most to the vulnerability of households in the KD and PD.

This study revealed the high vulnerability of the livelihoods of the nomads in the area studied, based on six components.

According to the UN study “Electronic Government—2020”, in the Open Government Development Index, the level of Kazakhstan is rated as “very high” [43].

From 1 January 2018, a forecast of unfavorable meteorological conditions for the next day is available on the website of RSE (Republican State Enterprise) “Kazhydromet”, which is updated every hour [44]. The nomads receive timely and advance information about natural disasters and climate change on their mobile phones. According to the survey, pastoralists in both districts received state support to purchase houses on loan on preferential terms, under the subsidy program for rural residents, as well as targeted social assistance to people in difficult life situations. Members of the HHs are members of associations, such as the Union of Farmers and the Association of Nature Users.

According to the results of the sixth edition of the Sustainable Development Report 2021, under the auspices of the “Decade of Action for the Sustainable Development Goals”, a positive trend with internet access in Kazakhstan is noted by members of the UN Sustainable Development Network [31]. However, the Ministry of Digital Development and Aerospace Industry states that, in rural areas, due to the lack of additional connections, the Internet speed remains low. In rural areas, internet speed is low due to the absence of additional connections. According to the survey results, 100% of nomads have mobile phones. Households on social networks receive 100% of the information about climate change and natural disasters (Table 2, Figure 2).

According to the Digital Technology Department of the Zhetysu Region, as of today, only 99.86% of residents in the region have access to the network. “At the moment, 287 rural settlements out of 352 have access to mobile internet. 65 do not have access. 135 rural settlements (38.4%) have access to 4G, and 287 rural settlements (81.5%) have access to 3G” [45].

About 60% of internet coverage in Kazakhstan is provided by land channels, optical fiber (approximately 26%), and copper (34%). The rest are radio channels: radio relay communication and satellite [46].

Agriculture has been and continues to be a priority sector of the Zhetysu region’s economy, as it provides employment for almost half of the region’s population. According to the Global Food Security Index “…Kazakhstan ranked 54th in terms of food availability, 35th—in terms of food availability and economic sufficiency, and 38th—in terms of food quality and safety” [47]. The assessment of the pastoralists’ vulnerability in both districts under the component “Agriculture and food security” is high. The higher percentage of livestock raised for food reflects greater sensitivity to environmental and climate change [48]. The proportion of HHs dependent on raising livestock for food is high in both districts.

The proportion of households dependent on livestock for food production is high in both districts (Table 2). An analysis of the dynamics of cattle, horses, sheep, and goats in the Kerbulak district from 1992 to 2022 shows that the cattle population increased from 46,600 to 59,500; the horse population increased from 11,600 to 22,400; while the sheep and goat population decreased from 384,100 to 258,200. In the Panfilov district, the cattle population increased from 62,700 to 89,300; the horse population increased from 11,100 to 22,500; while the sheep and goat population decreased from 510,800 to 284,200 (Figure 4) [49,50].

Agriculture has been and continues to be the priority sector of the economy in the Zhetysu region, as it provides employment for nearly half of the region’s population. As of the beginning of 2023, Kazakhstan’s total cattle population is 8.4 million head, sheep and goats amount to 19 million head, horses total 3.8 million head, and goats reach 2.3 million head. According to the Global Food Security Index, “…Kazakhstan ranks 54th in terms of food availability, 35th in terms of food availability and economic affordability, and 38th in terms of food quality and safety” [51].

The rate of HHs in both districts reporting livestock deaths due to snow leopards is quite high. On 3 January 2022, residents of the KD found a single individual of a snow leopard, which, when a person approached, did not run away and did not try to hide. Later, the park inspectors, together with researchers from the Institute of Zoology of the CS MES RK (science committee of the ministry of science and higher education of the Republic of Kazakhstan), immobilized the animal and transported it to the park, after installing a satellite collar (GPS). The survey identified numerous cases of conflict between pastoralists and snow leopards in 2019–2022. Shepherds in both districts have reported incidents of snow leopard attacks on livestock in the paddock. According to the data of nomads, up to 3–4 snow leopards perish annually in the study area at the hands of shepherds. Cattle breeders do not deny that, in retaliation for the dead cattle, they are ready to kill a predator in order to prevent future attacks, even knowing about the criminal liability for killing a Red Book animal. Shepherds told about the cases in 2021–2022 of two killed snow leopards with collar satellite equipment. A shepherd said that dozens of large and small livestock had already died from wolf attacks. Wild predators suffering from a lack of food in their natural habitat attack livestock. Similar cases have been registered in three villages, where unknown predators killed about 50 sheep. All the respondents indicated loss of livestock and crops due to natural disasters.

Permanent houses are more resilient to the impacts of natural disasters [52]. In the period 2017–2021, as part of state support measures for pastoralists in both districts, it was possible to improve living conditions thanks to the provided loans.

The assessment of the pastoralists’ vulnerability in both districts for the component “Finance and income” is extremely average in the KD and high in the PD. Access to loans improves the ability to cope with stress and is not associated with social inclusion [53] and well-being [54].

A detailed survey revealed that 100% of cattle breeders in both districts have loan debt connected with the state housing program’s implementation, consumer loans, car loans under a preferential program, or purchasing goods on an interest-free installment plan. According to experts from the Kazakhstan Association of Financiers, “…the rise in the cost of goods leads to a nominal increase in loans, and the growth in lending contributes to inflation acceleration”. To date, a special Law has been adopted to solve the problem [55]. Indian scientists Sangeeta et al. (2022) and Agrawal et al. (2009) conducted a study of the relationship between financial literacy and financial well-being, and it was concluded that financial attitude and financial behavior have a greater impact on people’s financial well-being [32,56]. In 2023, the “Karyzsyz kogam” project is being implemented, to help those who have overdue debts. The project aims to reduce vulnerability to financial fraud, and counteract the accumulation of excessive debts as well as to improve financial culture in rural areas [57]. A survey on HHs’ access to financial resources showed that nomads in both the PD and KD do not have access to financial resources due to several factors. The first factor is the lack of collateral, and the balance of the loan payment’s total amount was below the subsistence level. In rural areas of the Zhetysu region, houses are often built from adobe. Due to the fragility of the materials exploited, financial institutions do not use such houses as collateral. The second factor is the high interest rate on the loan and the short grace period for the principal debt repayment. Within the framework of the Auyl Amanats project, microloans will be issued for villagers at 2.5% per annum for a period of 5–7 years and with a one-year grace period on the principal debt [58].

The LVI-IPCC method revealed the adaptive ability, sensitivity and susceptibility of the nomads of the Kerbulak and Panfilov districts of the Zhetysu region of Kazakhstan, which can be used “…when developing strategic and program documents, and including the necessary measures to adapt to climate change at the national and local levels” [18]. Vulnerability to climate change can be broadly defined, according to T. Kushnarenko et al., “…as a function of socio-economic (adaptive potential) and biophysical (sensitivity and impact) factors in the process of providing livelihoods at the household level” [59].

Adaptation to climate change is seen as one of the key elements of Kazakhstan’s climate change policy. For the first time in the legislation of Kazakhstan, “…norms have been introduced for adaptation to climate change in priority sectors: water resources, agriculture, forestry and disaster risk reduction. Chapter 22 of the adopted Environmental Code is devoted to the issues of state regulation in the field of adaptation to climate change. The Rules to organize and implement the process of adaptation to climate change have been approved…Based on the results of the vulnerability assessment, it will be possible to consider the impact of climate change in the development of strategic and program documents, and include the necessary measures for adaptation to climate change at the national and local levels” [60].

As well as providing the necessary financial resources for measures to mitigate the impact on the climate through the inclusion of actions on adaptation to climate change in strategic and program documents; development and adoption of the Social Code to improve and develop policies and legislation in the field of social security, as well as bringing them into line with international standards and recommendations; it is also necessary to raise the problem of the low qualifications and knowledge of pastoralists at the state level through the creation and implementation of an information system by the non-profit joint-stock company “National Agrarian Scientific and Educational Center” of the Ministry of Agriculture of the Republic of Kazakhstan, as well as at the legislative level by fixing the provision of the continuity of environmental education from primary preschool and school age; as well as the elimination of the root causes of livestock vulnerabilities through pastoral participation in decision-making processes and access to resources and services that can ensure their well-being. The insurance of farm animals through the KazAgroGarant JSC (Joint-stock company) of the Ministry of Agriculture of Kazakhstan can be considered as one of the measures aimed at reducing damage from adverse weather events. As well as the continued gasification of regions with coverage of individual residential buildings; a gradual decrease in the volume of fossil fuels burned and the transition to the use of electricity heat instead of the direct combustion of fossil fuels, as well as the increased use of natural gas and renewable energy sources; it is also necessary to introduce space monitoring for all types of natural emergencies.

Successful adaptation to global climate change depends not only on government policy, but also on the active, continuous involvement of other participants in this process, including national, regional, international organizations, representatives of the public and private sectors, and civil society. There is also the consideration of the possibility of introducing a rating that reflects information about companies that comply with the principles of a responsible attitude to the environment, social responsibility and quality of corporate governance.

5. Conclusions

Within the scope of this assessment, we have analyzed the vulnerability of habitats among nomadic communities in the mountainous regions of the Zhetysu region in Kazakhstan. The key aspect of this study is the identification of the Livelihood Vulnerability Index (LVI) through the application of fourteen primary components and fifty-six subcomponents, as well as vulnerability indices (LVI-IPCC) using three elements: exposure, sensitivity, and adaptive capacity. Natural disasters driven by drought, temperature fluctuations, and precipitation variations significantly increase the vulnerability of nomadic households living in isolated highland areas with a complex infrastructure.

In both regions, nomadic vulnerability is high in components such as natural resources, human–wildlife conflict, housing type, agriculture, food security, and social networks. According to the “Finance and income” indicator, only pastoralists in the Panfilov district exhibit high vulnerability.

Identifying the levels of vulnerability of nomadic households to climate change and understanding their adaptation strategies will enable livestock herders to access new means of reducing livelihood vulnerability.

Strategies to mitigate vulnerability related to natural resources involve insuring livestock against natural disasters or environmental risks, such as fires, wildlife attacks, and others. Until now, livestock insurance has been virtually absent in the country.

In the territories of the Panfilov and Kerbulak districts of the Zhetysu region, there is the state national natural park “Altyn-Emel,” which is a UNESCO World Heritage Site. The strategy to reduce the conflict between humans and wildlife involves engaging indigenous populations in nature conservation activities, educating nomads on the importance and prospects of protecting Red Book species such as snow leopards, and providing them with sustainable financial resources when they refrain from hunting rare animals.

One way to reduce the financial vulnerability of nomads is to implement adaptive strategies by diversifying livelihoods, such as highland ecotourism in their residential areas.

The strategy for reducing financial vulnerability through improved financial literacy includes organizing targeted seminars and training sessions to provide information about low-interest rate loans (2.5%) under the “Auyl Amanat” project, financial planning, equipping individuals with knowledge, risk-identification skills, and risk-mitigation measures. The strategy also includes scaling up the pilot project to increase rural income and agricultural production for rural business financing at 2.5% for various areas, such as effective use of household plots for livestock and crop production, efficient use of land resources outside of settlements legally owned by rural residents, explaining the mechanism of livestock subsidies, and preparing documents for subsidy applications. This will allow livestock herders to understand the functions of subsidies, and the possibility of obtaining them under certain conditions, then create these conditions themselves, cover part of the production costs, and increase business profitability. Training in a cooperative organization with legal status will ensure stable product sales and income growth.

The research results will serve as a basis for taking measures within the framework of the development and implementation of state programs for climate adaptation in accordance with the Environmental Code of the Republic of Kazakhstan, where agriculture is one of the priority management directions.