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  <front>
    <journal-meta><journal-id journal-id-type="publisher">ADGEO</journal-id><journal-title-group>
    <journal-title>Advances in Geosciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ADGEO</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Adv. Geosci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1680-7359</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/adgeo-45-217-2018</article-id><title-group><article-title>Spatial assessment of the distribution and potential of bioenergy resources in Kazakhstan</article-title><alt-title>Spatial assessment of the distribution and potential of bioenergy resources in Kazakhstan</alt-title>
      </title-group><?xmltex \runningtitle{Spatial assessment of the distribution and potential of bioenergy resources in Kazakhstan}?><?xmltex \runningauthor{A. Koshim et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Koshim</surname><given-names>Asima</given-names></name>
          <email>asima.koshim@kaznu.kz</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff3">
          <name><surname>Karatayev</surname><given-names>Marat</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff4">
          <name><surname>Clarke</surname><given-names>Michèle L.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Nock</surname><given-names>William</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Department of Cartography, Al-Farabi Kazakh National University, Al-Farabi Avenue, 71, 050040, Almaty, Kazakhstan</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Energy Technologies Research Institute, The University of Nottingham, Nottingham, NG8 1BB, UK</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>The Water-Energy-Food Nexus Partnership Program, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>School of Geography, The University of Nottingham, Nottingham, University Park, NG7 2RD, UK</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB2 1TN, UK</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Asima Koshim (asima.koshim@kaznu.kz)</corresp></author-notes><pub-date><day>28</day><month>August</month><year>2018</year></pub-date>
      
      <volume>45</volume>
      <fpage>217</fpage><lpage>225</lpage>
      <history>
        <date date-type="received"><day>23</day><month>May</month><year>2018</year></date>
           <date date-type="rev-recd"><day>9</day><month>August</month><year>2018</year></date>
           <date date-type="accepted"><day>13</day><month>August</month><year>2018</year></date>
      </history>
      <permissions>
        
        
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018.html">This article is available from https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018.html</self-uri><self-uri xlink:href="https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018.pdf">The full text article is available as a PDF file from https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018.pdf</self-uri>
      <abstract>
    <p id="d1e130">Kazakhstan is naturally rich in fossil fuels and its economy is strongly
linked to oil and gas exports. Significant coal reserves have led to an
energy mix that is dominated by aging and polluting thermal power plants. Yet
Kazakhstan comprises mainly grassland steppe where agriculture and livestock
pastoralism dominate offering the potential for cleaner, renewable energy
production from a range of agricultural and forestry wastes. Here we analyse
the spatial distribution and bioenergy generation potential of different
feedstocks using an ArcGIS platform and demonstrate a significant opportunity
for a range of bioenergy technologies. We recommend a number of policy
interventions to enable Kazakhstan to make a transition to cleaner, more
accessible and locally generated supply which is also sustainable and provide
a waste management solution.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p id="d1e137"><bold>(a)</bold> Administrative provinces of Kazakhstan;
<bold>(b)</bold> mean annual precipitation; <bold>(c)</bold> electricity capacity
generation by fuel type; <bold>(d)</bold> electric power zones;
<bold>(e)</bold> annual energy demand by regions; <bold>(f)</bold> major areas of
wheat production.</p></caption>
      <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018-f01.jpg"/>

    </fig>

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p id="d1e168">Kazakhstan is a large steppe country located in Central Asia. It is
surrounded by Turkmenistan, Uzbekistan, and Kyrgyzstan to the south, Russia
to the north, China to the east, and is bounded to the west by the Caspian
Sea. The country has a total land area of 2.7 million km<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>, sub-divided
into fourteen administrative provinces (Fig. 1a), each governed from a local
centre, usually the largest local city, which is called the capital of that
province. Generally, the climate of Kazakhstan is extreme continental with
average temperature between <inline-formula><mml:math id="M2" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>4 and <inline-formula><mml:math id="M3" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>19 <inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in January and between
<inline-formula><mml:math id="M5" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>19 and <inline-formula><mml:math id="M6" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>26 <inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in July. The country is almost entirely covered
by steppe and desert with brown and grey-brown soils; the northern area of
Kazakhstan is the wettest region, where precipitation exceeds
300 mm yr<inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (Fig. 1b), mainly because of afternoon thunderstorms that
occur in summer, while in the centre and south, precipitation is around
150–200 mm yr<inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (KIG, 2013). According to Kazakhstan's Agency of
Statistics, the country has an estimated population of around 17.8 million
(KAS, 2016) and 60 % of people live in cities (Nyussupova and
Kalimurzina, 2016). The energy sector plays a major role in Kazakhstan's
economic development. The country has abundant reserves of fossil fuel (oil,
gas and coal) and proven gas reserves estimated at about 3.8 trillion m<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> (EIA, 2017). Natural gas
and clean energy technologies have the potential to contribute to reducing
the country's carbon footprint (Kerimray et al., 2018); there are plans to
build additional gas infrastructure that will bring natural gas to
Kazakhstan's capital of Astana, and large cities in the North and Central
Kazakhstan (MoE, 2018). Gas grid connectivity is expected to reach 56 %
of the population by 2030, compared to 47.3 % currently (MoE, 2018). The
potential oil resources in Kazakhstan are estimated to be over 30 billion
barrels and Kazakhstan is a strongly export dominated economy with between
75 %–80 % of its total oil production exported overseas. In 2016,
oil production was around 79.4 Mt against a consumption of 14.6 (EIA, 2017).
Kazakhstan has also significant coal reserves, especially in central part of
Kazakhstan. Its total recoverable coal reserves were estimated in 2015 at
around 176.7 million short tons (EIA, 2017). Despite the abundance of oil
and gas, the country's power generation has been dominated by coal. By the
end of 2016, coal-fired thermal power in Kazakhstan comprised 75 %
(15 GW) of the total installed capacity and contributed 80 % (82.4 TWh)
of total generated electrical energy, mainly concentrated close to the coal
fields in the North of Kazakhstan (Fig. 1c). The North and Central regions of
Karaganda, Pavlodar, Akmola, Kostanay, Aktobe, East Kazakhstani provinces
have a 220–500–1150 kV electric grid that is connected to Russia's
electricity system (Fig. 1d) and these provinces have higher electricity
consumption than in the West and South (Fig. 1e) (KEGOC, 2016). Coal is
expected to play a dominant role in national energy balance at least for the
next 30–40 years (Kazenergy, 2017). The domination of coal in Kazakhstan's
current energy balance coupled with its aging plant and unequal spatial
distribution networks (including electricity production) contributes both to
regional energy price inequalities (Assembayeva et al., 2018) and to ongoing
serious environmental<?pagebreak page219?> damage (Kerimray et al., 2017). Coal power stations are
responsible for almost 80 % of the country's carbon emissions (ICSD,
2016). The quality of Kazakhstani coal is relatively poor, with a high ash
content of between 50 % to 65 % (EIA, 2017). Given current and
projected future coal use in Kazakhstan, and the need to cut emissions,
biomass co-firing in coal power stations is regarded as a promising option
which could enable Kazakhstan to cut its carbon emissions and provide an
improved sustainable energy supply.</p>
      <p id="d1e260">Currently, there are three co-firing technologies widely used in power
plants: direct co-firing, indirect co-firing, and parallel co-firing. In
direct co-firing, biomass is directly fed into the furnace along with base
fossil fuel (e.g. coal). Indirect co-firing installs a separate gasifier to
convert the solid biomass into a fuel gas (syngas). Parallel co-firing
involves the installation of a completely separate external biomass-fired
boiler in order to produce steam used to generate electricity in the
coal-fired power plant. Biomass co-firing does not require significant
capital investment and has the advantage of being able to use existing
fossil fuel power plants infrastructure (Roni et al., 2017). Biomass
co-firing can replace between 20 %–50 % of coal and reduces carbon dioxide
emission from the power plant; the amount of other pollutants released will
depend on the characteristics of the biomass feedstock (Lempp, 2013).</p>
      <p id="d1e263">Kazakhstan has various biomass resources including forest wood and
agricultural residues. While the spatial distribution and potential of solar
and wind energy has been previously demonstrated (UNDP, 2015, 2018;
Terehovics et al., 2017; Ahmad et al., 2017) to date there has not been any
analysis of bioenergy feedstocks for potential use in energy generation in
Kazakhstan. Here we present the available biomass resources (crop residues,
the production of livestock residues and forest residues) and then evaluate
for the first time the total amount and the spatial distribution of the crop
residue availability for energy generation. This type of assessment is needed
by policy makers and local governments in their efforts to establish medium-
and long-term planning for the development of biomass and biogas resources,
and for enterprises and planners to arrange for the development and
implementation of projects related to biomass resources.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><caption><p id="d1e268"><bold>(a)</bold> Areas of fibre and non-grain crop production;
<bold>(b)</bold> major areas of fodder crops production;
<bold>(c)</bold> availability of crop residues; <bold>(d)</bold> number of livestock
per 100 ha; <bold>(e)</bold> forest cover in Kazakhstan; <bold>(f)</bold> energy
generation potential from crop residues.</p></caption>
        <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018-f02.jpg"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <title>Available bioenergy resources</title>
<sec id="Ch1.S2.SS1">
  <title>Crop residues</title>
      <p id="d1e305">In 2016, Kazakhstan produced nearly 27 million tons of crops (KAS, 2017).
The UN FAO estimated that the maximum crop production from Kazakhstan in good
years exceeds 35–40 million tons (FAO, 2016). Wheat is the main crop in
Kazakhstan, producing around 14–16 million tons of grain annually (KAS,
2017). Around 75 %–80 % of Kazakhstan wheat production occurs in
North Kazakhstan including Akmola, Kostanay, and Northern Kazakhstani
provinces, with a small amount being produced in the south (Fig. 1f). The
east and south of Kazakhstan are area of fibre and non-grain crop production
includes cotton, flax, sugar beet, and tobacco (Fig. 2a). The south, east and
central Kazakhstan is also major area of fodder crop production including
barley, oil seed rape, grass, maize, millet, soya beans and oats (Fig. 2b).
According to our data, from 2004 to 2016, the total plant based agricultural
produce (including grain, cobs, seeds, straw, husks, chaff) has been
estimated to be around 16.4 Mt yr<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. The agricultural residue
available for bioenergy production (straw, cobs, husks) is about
8.2 Mt yr<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. The principle locations of agricultural wastes which can
be converted to energy production are shown in Fig. 2c. The highest
percentage of residues is found in the North regions of Akmola, Kostanay, and
Northern Kazakhstani provinces, followed by the East and South regions, each
containing more than 1.2 million tons of residues per year available for
bioenergy. Approximately 7 % of this is used as livestock fodder and
bedding; the rest is buried (KAS, 2017), burned or ploughed in.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Animal wastes</title>
      <p id="d1e338">Organic-rich animal wastes are increasingly used for
bioenergy generation in anaerobic digestion plants which produce biogas
(principally methane and carbon dioxide) and digestate residue which can be
used as an agricultural fertiliser. The animal waste residues produced in
Kazakhstan depends on the animal type, size, and population density for each
location. Livestock production in Kazakhstan contributes to rangeland
management and for example the total cattle herd was estimated at about
6.204 million head in 2016 (KAS, 2017). For energy production, animals need
to be housed for some of the year in order to make residue collection cost
effective. Livestock are spatially distributed driven by climatic conditions
which controls grass cover and tend to be found principally in the eastern,
northern and southern provinces of Kazakhstan, while the west and central
regions fewer livestock numbers (Fig. 2d). For cattle manure, Kazakhstan's
annual production is about 45 million tons, mostly from the South-Eastern
region of Kazakhstan followed by North region and Central region (KAS,
2017).</p>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Forestry</title>
      <p id="d1e347">In Kazakhstan, the forested area occupies around 4.7 %
(over 12 million ha) of the land area of the country, and of this about
40 % is estimated to host productive forestry (FAO, 2016). The total
standing timber stock is 375.7 million m<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> (KAS, 2017) and forests
produce between 0.8 and 1.0 million m<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> of timber annually (KAS, 2017).
The region with largest forest cover is in Kyzylorda in mountainous south
Kazakhstan and woodlands are also prevalent in the south-eastern regions,
though not all of these will be suitable for forestry (Fig. 2e). According
to Kazakhstan's<?pagebreak page220?> Agency of Statistics, the largest productive standing timber
stock is concentrated in the eastern region of Kazakhstan (44.5 %),
Kyzylorda in the south (10.5 %), Akmola (11.4 %) and Pavlodar provinces
(7.1 %) in the north. These locations have the potential for biomass
co-firing through creative of wood chips and wood pellets.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Bioenergy generation potential</title>
<sec id="Ch1.S3.SS1">
  <title>Estimation and mapping bioenergy resources</title>
      <p id="d1e380">Due to issues with the availability of data, this paper focuses on crop
residues. Theoretical energy potential from crop residues is analysed via an
ArcGIS platform, according to the following steps:
<list list-type="order"><list-item>
      <p id="d1e385">Selection of agricultural crops</p>
      <p id="d1e388">The crops considered in this study whose residues that have potential to be
used feedstocks for bioenergy production include wheat, rice, oil-bearing
crops (e.g. oilseed rape), cotton, sugar crops, tubers, and beans. Data on
production by province from 2004 to 2016 was obtained from Kazakhstan's
Agency of Statistics (KAS, 2017).</p></list-item><list-item>
      <p id="d1e392">Calculation of total yield of crop residues</p>
      <p id="d1e395">Total values of crop residues were estimated using the following
mathematical model:<disp-formula id="Ch1.Ex1"><mml:math id="M15" display="block"><mml:mrow><mml:mi mathvariant="normal">CRs</mml:mi><mml:mfenced close=")" open="("><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mi mathvariant="normal">CBA</mml:mi><mml:mfenced open="(" close=")"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:mi mathvariant="normal">RPR</mml:mi><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></disp-formula>where:
CRs<inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is total yield of <inline-formula><mml:math id="M17" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crop residues at <inline-formula><mml:math id="M18" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula> province, ton;
CBA<inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is total yield of <inline-formula><mml:math id="M20" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crop production at <inline-formula><mml:math id="M21" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula> province, ton;
RPR<inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the residue production ratio of <inline-formula><mml:math id="M23" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crop residues at <inline-formula><mml:math id="M24" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula>
province;</p>
      <p id="d1e544">The crop-to-residues ratios were derived from FAO's Bioenergy and Food
Security Rapid Appraisal Tool (FAO, 2017).</p></list-item><list-item>
      <p id="d1e548">Calculation of bioenergy potential</p>
      <p id="d1e551">Bioenergy potential from crop residue biomass was estimated using the
following mathematical model:<disp-formula id="Ch1.Ex2"><mml:math id="M25" display="block"><mml:mrow><mml:mi>E</mml:mi><mml:mfenced open="(" close=")"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mi mathvariant="normal">CRs</mml:mi><mml:mfenced open="(" close=")"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:mi mathvariant="normal">TV</mml:mi><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></disp-formula>where:
<inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:mi>E</mml:mi><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is bioenergy potential of <inline-formula><mml:math id="M27" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crops at <inline-formula><mml:math id="M28" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula> province, MJ;
CRs<inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is total yield of <inline-formula><mml:math id="M30" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crop residues at <inline-formula><mml:math id="M31" display="inline"><mml:mi>j</mml:mi></mml:math></inline-formula> province, ton;
TV<inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is thermal value of <inline-formula><mml:math id="M33" display="inline"><mml:mi>i</mml:mi></mml:math></inline-formula> crop residues, MJ t<inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>.</p>
      <p id="d1e707">Thermal values of residues were taken from FAO's Bioenergy and Food Security
Rapid Appraisal Tool (FAO, 2017).</p>
      <p id="d1e710">Both mathematical models were used in previous studies to assess bioenergy
potential, for example, in China (Jiang et al., 2012), Russia (Namsaraev et
al., 2018), India (Hiloidhari et al., 2014) and Egypt (Kamel et al., 2018).</p></list-item><list-item>
      <p id="d1e714">Mapping the results</p>
      <p id="d1e717">An ArcGIS approach has been adopted in this study to map the potential
regions for bioenergy production across Kazakhstan. ArcGIS has been used in
many bioenergy studies for this purpose including India (Ramachandra and
Shruthi, 2007), Northern Europe (Kukk et al., 2011), China (Zhuang et al.,
2011; Chang et al., 2014), Mexico (Valdez-Vazquez et al., 2010), Iran
(Noorollahi et al., 2015), Zambia (Shane et al., 2016) and Uganda (Twaha et
al., 2016).</p></list-item></list></p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><caption><p id="d1e722"><bold>(a)</bold> Degree of water stress (WRI, 205); <bold>(b)</bold> degree
of soil degradation (KIG, 2013); <bold>(c)</bold> current renewable energy
projects; <bold>(d)</bold> future renewable energy projects as a part of
Renewable Energy Action Plan for 2012–2030.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://adgeo.copernicus.org/articles/45/217/2018/adgeo-45-217-2018-f03.jpg"/>

        </fig>

</sec>
<?pagebreak page221?><sec id="Ch1.S3.SS2">
  <title>Theoretical bioenergy potential</title>
      <p id="d1e748">Based on net biomass yield from different residues and its conversion
efficiency (thermal values of each type of residues), the bioenergy potential
in Kazakhstan is estimated at 485.36 MJ (or 16.582 million tons of coal
equivalent, with an average of 14.150 Mt yr<inline-formula><mml:math id="M35" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the past 12 years)
(Table 1). This is equivalent to around 30 % of the total current energy
consumption of the country. In Kazakhstan, biomass energy resource mainly
consists of wheat residues (44 %), produced in the Northern part of
Kazakhstan. Consequently, the highest potential for biomass co-firing is
found in the northern part of Kazakhstan in Akmola, Kostanay, and other
northern provinces (Fig. 2f). The biomass energy potential from wheat
residues is found to be 3612.0 Mt yr<inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in Akmola,
3101.4 Mt yr<inline-formula><mml:math id="M37" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in Kostanay and 2846.3 Mt yr<inline-formula><mml:math id="M38" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> other northern
provinces. Given that coal-fired power plants are also prevalent in these
regions, this demonstrates the great potential for adopting the use of
biomass co-firing technologies in this part of Kazakhstan, with concomitant
impacts on emissions leading to reduced greenhouse gas release (see Figs. 1c
and 2f). The southern part of Kazakhstan (Almaty and Southern Kazakhstanskaya
provinces), also shows potential with a maximum of 2105.5 Mt of biomass
residues per year.</p>
</sec>
<sec id="Ch1.S3.SS3">
  <title>Limiting factors for bioenergy development</title>
      <p id="d1e805">Bioenergy production from agricultural residues is hugely dependent on water
availability (including precipitation) and land resources. Kazakhstan is the
most water stressed country in the world, according to the Water Stress Index
(WSI, 2015). Of the 186 countries studied, Kazakhstan is on the top of the
list of 35 most likely countries to experience an interruption to water
supply (WSI, 2015). As shown on Fig. 3a, Northern and South-Eastern zones of
Kazakhstan have the largest areas with extremely high-water stress. This
means that droughts are expected to occur on average every five years in
these regions and the present hazard level may increase in the future due to
the effects of climate change (Salnikov et al., 2015). Furthermore,
Kazakhstan has been experiencing soil erosion and degradation due to
salinization, acidification, and contamination by anthropogenic pollution.
According to KIG (2013), most of territory of Kazakhstan, 179.9 out of
272.5 million ha (66 % of total area) has moderate level of soil
degradation and 31.3 % of total irrigated lands are salinized and all
degraded arable lands have lost soil organic carbon (humus; Fig. 3b). There
are areas with high level of soil degradation across western, northern and
south-eastern zones of Kazakhstan which challenges agricultural productivity.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1"><caption><p id="d1e811">Annual estimates of bioenergy potential from crop residues.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.93}[.93]?><oasis:tgroup cols="4">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Crop</oasis:entry>
         <oasis:entry colname="col2">Residue</oasis:entry>
         <oasis:entry colname="col3">Equivalent</oasis:entry>
         <oasis:entry colname="col4">Energy</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">(Mt)</oasis:entry>
         <oasis:entry colname="col3">standard coal (Mt)</oasis:entry>
         <oasis:entry colname="col4">potential (MJ)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">2004</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Wheat</oasis:entry>
         <oasis:entry colname="col2">10 930.7</oasis:entry>
         <oasis:entry colname="col3">5465.4</oasis:entry>
         <oasis:entry colname="col4">159 972.2</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Oil-bearing crops</oasis:entry>
         <oasis:entry colname="col2">791.6</oasis:entry>
         <oasis:entry colname="col3">418.8</oasis:entry>
         <oasis:entry colname="col4">12 258.2</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Sugar crops</oasis:entry>
         <oasis:entry colname="col2">39.8</oasis:entry>
         <oasis:entry colname="col3">17.5</oasis:entry>
         <oasis:entry colname="col4">512.2</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Cotton</oasis:entry>
         <oasis:entry colname="col2">1401.3</oasis:entry>
         <oasis:entry colname="col3">760.9</oasis:entry>
         <oasis:entry colname="col4">22 271.5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Tubers</oasis:entry>
         <oasis:entry colname="col2">2059.3</oasis:entry>
         <oasis:entry colname="col3">1000.8</oasis:entry>
         <oasis:entry colname="col4">29 293.4</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beans</oasis:entry>
         <oasis:entry colname="col2">2341.8</oasis:entry>
         <oasis:entry colname="col3">1271.6</oasis:entry>
         <oasis:entry colname="col4">37 219.7</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Rice</oasis:entry>
         <oasis:entry colname="col2">1059.7</oasis:entry>
         <oasis:entry colname="col3">454.6</oasis:entry>
         <oasis:entry colname="col4">13 306.1</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Total</oasis:entry>
         <oasis:entry colname="col2">18 624.2</oasis:entry>
         <oasis:entry colname="col3">9389.6</oasis:entry>
         <oasis:entry colname="col4">274 833.1</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">2016</oasis:entry>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Wheat</oasis:entry>
         <oasis:entry colname="col2">16 483.9</oasis:entry>
         <oasis:entry colname="col3">8242.0</oasis:entry>
         <oasis:entry colname="col4">241 243.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Oil-bearing crops</oasis:entry>
         <oasis:entry colname="col2">3804.8</oasis:entry>
         <oasis:entry colname="col3">2012.7</oasis:entry>
         <oasis:entry colname="col4">58 911.7</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Sugar crops</oasis:entry>
         <oasis:entry colname="col2">34.5</oasis:entry>
         <oasis:entry colname="col3">15.2</oasis:entry>
         <oasis:entry colname="col4">444.9</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Cotton</oasis:entry>
         <oasis:entry colname="col2">860.1</oasis:entry>
         <oasis:entry colname="col3">467.0</oasis:entry>
         <oasis:entry colname="col4">13 669.1</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Tubers</oasis:entry>
         <oasis:entry colname="col2">3795.2</oasis:entry>
         <oasis:entry colname="col3">1844.5</oasis:entry>
         <oasis:entry colname="col4">53 988.5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beans</oasis:entry>
         <oasis:entry colname="col2">5428.0</oasis:entry>
         <oasis:entry colname="col3">2947.4</oasis:entry>
         <oasis:entry colname="col4">86 270.4</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Rice</oasis:entry>
         <oasis:entry colname="col2">2456.1</oasis:entry>
         <oasis:entry colname="col3">1053.7</oasis:entry>
         <oasis:entry colname="col4">30 841.8</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Total</oasis:entry>
         <oasis:entry colname="col2">32 862.6</oasis:entry>
         <oasis:entry colname="col3">16 582.5</oasis:entry>
         <oasis:entry colname="col4">485 368.7</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

<?xmltex \hack{\newpage}?>
</sec>
</sec>
<?pagebreak page222?><sec id="Ch1.S4">
  <title>Bioenergy policy in Kazakhstan and some recommendations</title>
      <p id="d1e1140">To promote the development of renewable energy in Kazakhstan, the government
issued the National Green 2050 Economy Concept in 2013 (no. 577 of 30 May
2013), followed by the Law on Green Economy in 2016 (no. 506-V of 28 April
2016) and the Renewable Energy Action Plan for 2012–2030 (Directive no. 068
of 24 February 2017). Kazakhstan has set the target of raising the share of
renewable resources in electricity production from 3 % by 2020 to
50 % by 2050 (Karatayev and Clarke, 2016). These targets and government
strategies encourage the uptake and commercialisation of biogas,
biomass-derived liquid fuels and biomass generated electricity sectors
(Karatayev et al., 2017). The National Parliament has approved a Law on the
Regulation of Production of Energy from Biomass Resources no. 351-IV and
National Tariffs for Energy Produced from Biomass Resources (Directive
no. 645 of 12 June 2014). In 2016, the Ministry of Industry and New
Technologies proposed four projects of biofuel production in North and South
Kazakhstan (Fig. 3c and d). However, the development of a modern bioenergy
sector in the country is still limited due to a number of specific barriers
that resource-rich countries currently face (Karatayev et al., 2016). The
current availability of fossil fuels coupled with insufficient political will
have hindered the development of the renewable energy sector in Kazakhstan
(Karatayev et al., 2016). Artificially low electricity prices, subsidised up
to 60 % by the government, continues to be one of the main drivers of
energy policy in Kazakhstan. In order to enable development of the bioenergy
industry in Kazakhstan we recommend:
<list list-type="order"><list-item>
      <p id="d1e1145">Biomass co-firing technologies should be included in national energy
targets, programmes and legislation especially given that existing power
plants can be easily adapted. The European Union countries adopted the 2020
Energy and Climate Directive (Directive 2009/29/EC) setting three key
targets including a 20 % cut in greenhouse gas emissions (from 1990
levels), 20 % of EU energy from renewables and 20 % improvement in
energy efficiency. The Directive on Renewable Energy (2001/77/EC) provides
the framework for electricity production from biomass and the Biomass Action
Plan (2005/628/EC) states that electricity can be generated and co-fired
from all types of biomass, by mixing<?pagebreak page223?> it with coal or natural gas, or to run
freestanding power stations. At a national level, the UK adopted the
National Bioenergy Strategy that aims to promote generation of heat and
electricity from biomass resources. Additionally, the UK developed the
Energy Crop Scheme that provide farmers with grants for establishing energy
crops such as short rotation coppice and miscanthus. Currently, all major
coal-fired power plants in UK have adopted biomass co-firing. On average,
these plants fire 5 %–10 % biomass by energy basis. Most plants began their
co-firing experience with co-milling trials and then moved on to establish
direct injection systems for commercial production. A variety of feedstock
has been utilized in UK's coal combusting plants, including: agriculture
residues, energy crops and forestry residues. This provides a good model for
Kazakhstan to emulate.</p></list-item><list-item>
      <p id="d1e1149">Green Certificate Policies with bioenergy targets should be applied to
the power generation sector. Both dedicated bioenergy and co-generation
plants in the UK are in receipt of Renewable Energy Certificates (RECs) and
Renewable Obligation Certificates (ROCs). RECs represent a contractual right
of the holder to claim any benefit that is associated with energy created
from renewable sources. They are sometimes known as “Green Tags” or
“Renewable Energy Credits”. Each REC certifies that a single megawatt-hour
of electricity was generated from renewable sources. The ROCs are green
certificates issued to operators of accredited renewable generating stations
for the eligible renewable electricity they generate. Operators can trade
ROCs with other parties. We propose that Kazakhstan adopt a similar scheme.</p></list-item><list-item>
      <p id="d1e1153">It is essential to create an effective logistics planning and resource
supply chain which incentivises the re-purposing of forest, agricultural and
livestock wastes for energy generation. According to Sahay (2003), there are
three types of supply chain collaboration to enable visibility; firstly,
between raw material suppliers; secondly between manufacturers and retailers
and thirdly, collaboration between third parties. In bioenergy third party
collaboration, partnerships exists in both upstream and downstream sectors
of the supply chain. Examples of this type are also seen in the UK bioenergy
industry demonstrated by the type of arrangements between forestry providers
and CHP production. Supply chain and logistics planning is based at the
strategic level in the supply chain. Given the co-location of adequate
biomass resources to thermal power plant an improved supply chain is an
essential pre-requisite for the transition to a lower carbon economy through
biomass co-firing.</p></list-item></list></p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p id="d1e1162">Here we have demonstrated how ArcGIS combined with theoretical energy
potential offers decision-makers information on the availability and type of
biomass options for Kazakhstan. Biomass co-firing is an attractive option
for reducing carbon emissions as it can use existing power plant
infrastructure. In this paper, we have identified the main residues
available in Kazakhstan for bioenergy production as well as their
geographical distribution. The bioenergy potential is estimated about 485.36 MJ (or 16.582 Mt of coal equivalent), which account for about 30 % of
total current energy consumption of the country. Wheat residues constitute
the major part of Kazakhstan's bioenergy potential, accounting for 44 % of
the total bioenergy potential. The results also reveal an imbalance in the
spatial distribution of bioenergy resources, driven by climate and
topography. The northern and southern provinces of Kazakhstan have the
highest bioenergy potential from wheat residues, while the dryland central
provinces have the least potential. Aridity, salinization and land
degradation ensures that the western provinces are inappropriate for
bioenergy production from biomass. The top three provinces with the highest
crop biomass energy resources are Akmola, Kostanay, and North Kazakhstani
provinces, located in the north of Kazakhstan. Considering the fact that
main country's coal-fired power plants are located close to the biomass
source regions in the Northern part of Kazakhstan, it is feasible to apply
biomass co-firing technologies in this region of Kazakhstan. To ensure the
viability of such an initiative some additional work is now required to
estimate the: (i) technical energy potential from livestock<?pagebreak page224?> and forest
residues; (ii) logistics of the resource supply chain including geographical
accessibility, transportation cost and certain radius of the thermal plants;
(iii) carbon emission reduction potential under different scenarios on development
of bioenergy and policy actions in Kazakhstan; (iv) biomass energy subsidies and
costs.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p id="d1e1169">The datasets and high-definition maps generated during the
current study are available from the corresponding author on reasonable
request.</p>
  </notes><notes notes-type="authorcontribution">

      <p id="d1e1175">AK and MK conceived and designed the idea of the study.
AK and WN collected data, developed and performed the calculations. MLC and MK
analyzed the results, wrote and revised the manuscript. AK and MK developed the
ArcGIS-Tool and created a map series. All authors discussed the results and contributed to the manuscript.</p>
  </notes><notes notes-type="competinginterests">

      <p id="d1e1181">The authors declare that they have no conflict of
interest.</p>
  </notes><notes notes-type="sistatement">

      <p id="d1e1187">This article is part of the special issue “European Geosciences
Union General Assembly 2018, EGU Division Energy, Resources &amp; Environment
(ERE)”. It is a result of the EGU General Assembly 2018, Vienna, Austria,
8–13 April 2018.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1193">This research was supported by the British Council Newton Al-Farabi
Partnership Programme Grant to Marat Karatayev and Michèle L. Clarke “The potential application of
renewable energy for rural energy services and electrification in
Kazakhstan”. Additional support was provided by Kazakhstani Ministry of
Science's Grant AP051310298 “Sustainable development and the
water-energy-food nexus in Central Asian region”. The authors would like to
thank the anonymous reviewers who improved the manuscript and discussions
with participants of 2018 European Geosciences Union General Assembly
(Division Energy, Resources &amp; the Environment).<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Viktor Bruckman<?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><ref-list>
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    <!--<article-title-html>Spatial assessment of the distribution and potential of bioenergy resources in Kazakhstan</article-title-html>
<abstract-html><p>Kazakhstan is naturally rich in fossil fuels and its economy is strongly
linked to oil and gas exports. Significant coal reserves have led to an
energy mix that is dominated by aging and polluting thermal power plants. Yet
Kazakhstan comprises mainly grassland steppe where agriculture and livestock
pastoralism dominate offering the potential for cleaner, renewable energy
production from a range of agricultural and forestry wastes. Here we analyse
the spatial distribution and bioenergy generation potential of different
feedstocks using an ArcGIS platform and demonstrate a significant opportunity
for a range of bioenergy technologies. We recommend a number of policy
interventions to enable Kazakhstan to make a transition to cleaner, more
accessible and locally generated supply which is also sustainable and provide
a waste management solution.</p></abstract-html>
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Torkmahalleh, M. A., and Gallachóir, B. P. Ó.: Investigating the
energy transition to a coal free residential sector in Kazakhstan using a
regionally disaggregated energy systems model, J. Clean. Prod., 196,
1532–1548, 2018.
</mixed-citation></ref-html>
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Kukk, L., Roostalu, H., Suuster, E., Rossner, H., Shanskiy, M., and Astover,
A.: Reed canary grass biomass yield and energy use efficiency in Northern
European pedoclimatic conditions, Biomass Bioenerg., 35, 4407–4416, 2011.
</mixed-citation></ref-html>
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Lempp, P.: Biomass Co-Firing in Coal Power Plants. IEA-ETSAP and IRENA
technology policy brief, available at:
<a href="https://iea-etsap.org/E-TechDS/PDF/E21IR_Bio-cofiring_PL_Jan2013_final_GSOK.pdf" target="_blank">https://iea-etsap.org/E-TechDS/PDF/E21IR_Bio-cofiring_PL_Jan2013_final_GSOK.pdf</a>
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2018.
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Namsaraev, Z. B., Gotovtsev, P. M., Komova, A. V., and Vasilov, R. G.:
Current status and potential of bioenergy in the Russian Federation, Renew.
Sust. Energ. Rev., 81, 625–634, 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib25"><label>25</label><mixed-citation>
Noorollahi, Y., Kheirrouz, M., Asl, H. F., Yousefi, H., and Hajinezhad, A.:
Biogas production potential from livestock manure in Iran, Renew. Sust.
Energ. Rev., 50, 748–754, 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib26"><label>26</label><mixed-citation>
Nyussupova, G. and Kalimurzina, A.: The dynamics of sex-age structure of the
population in urban and rural areas in the Republic of Kazakhstan in the
years 1991–2013, Bulletin of Geography, 31, 87–111, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib27"><label>27</label><mixed-citation>
Ramachandra, T. V. and Shruthi, B. V.: Spatial mapping of renewable energy
potential. Renew. Sust. Energ. Rev., 11, 1460–1480, 2007.
</mixed-citation></ref-html>
<ref-html id="bib1.bib28"><label>28</label><mixed-citation>
Roni, M. S., Chowdhury, S., Mamun, S., Marufuzzaman, M., Lein, W., and
Johnson, S.: Biomass co-firing technology with policies, challenges, and
opportunities: A global review, Renew. Sust. Energ. Rev., 78, 1089–1101,
2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib29"><label>29</label><mixed-citation>
Sahay, B. S.: Supply chain collaboration: the key to value creation, Work
Study, 52, 76–83, 2003.

</mixed-citation></ref-html>
<ref-html id="bib1.bib30"><label>30</label><mixed-citation>
Salnikov, V., Turulina, G., Polyakova, S., Petrova, Y., and Skakova, A.:
Climate change in Kazakhstan during the past 70 years, Quatern. Int., 358,
77–82, 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib31"><label>31</label><mixed-citation>
Shane, A., Gheewala, S. H., Fungtammasan, B., Silalertruksa, T., Bonnet, S.,
and Phiri, S.: Bioenergy resource assessment for Zambia, Renew. Sust. Energ.
Rev., 53, 93–104, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib32"><label>32</label><mixed-citation>
Terehovics, E., Khabdullin, A., Khabdullin, A., Khabdullina, Z., Khabdullina,
G., Veidenbergs, I., and Blumberga, D.: Why Solar Electricity has High
Potential for Kazakhstan Industries, Enrgy. Proced., 113, 417–422, 2017.
</mixed-citation></ref-html>
<ref-html id="bib1.bib33"><label>33</label><mixed-citation>
Twaha, S., Ramli, M., Murphy, P., Mukhtiar, M., and Nsamba, H.: Renewable
based distributed generation in Uganda: resource potential and status of
exploitation, Renew. Sust. Energ. Rev., 57, 786–798, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib34"><label>34</label><mixed-citation>
UNDP Kazakhstan (UNDP): Wind Atlas 2015, available at:
<a href="http://atlas.windenergy.kz/" target="_blank">http://atlas.windenergy.kz/</a> (last access: 16 March 2018), 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib35"><label>35</label><mixed-citation>
UNDP Kazakhstan (UNDP): Solar Resources Atlas 2018, available at:
<a href="http://atlassolar.kz/" target="_blank">http://atlassolar.kz/</a>, last access: 30 June 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib36"><label>36</label><mixed-citation>
Valdez-Vazquez, I., Acevedo-Benítez, J. A., and Hernández-Santiago,
C.: Distribution and potential of bioenergy resources from agricultural
activities in Mexico, Renew. Sust. Energ. Rev., 14, 2147–2153, 2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib37"><label>37</label><mixed-citation>
Water Stress Index (WSI): The World's Most Water-Stressed Countries 2015,
available at: <a href="http://www.wri.org" target="_blank">http://www.wri.org</a>, last access: 10 July 2018.
</mixed-citation></ref-html>
<ref-html id="bib1.bib38"><label>38</label><mixed-citation>
Zhuang, D., Jiang, D., Liu, L., and Huang, Y.: Assessment of bioenergy
potential on marginal land in China, Renew. Sust. Energ. Rev., 15,
1050–1056, 2011.
</mixed-citation></ref-html>--></article>
