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Optimal herdsmen household management modes in a typical steppe region of Inner Mongolia, China
Journal of Cleaner Production 231 (2019) 1e9
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Optimal herdsmen household management modes in a typical steppe region of Inner Mongolia, China Qing Zhang a, Yanyun Zhao a, b, * , Frank Yonghong Li a a Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China b Grassland Research Institute of Chinese Academy of Agricultural Sciences, Hohhot, 010021, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 December 2018 Received in revised form 28 April 2019 Accepted 18 May 2019 Available online 20 May 2019
Livestock systems account for 19% of total global greenhouse gas emissions, so it is of great importance to explore their carbon footprint under different management modes for reducing greenhouse gas emissions and mitigating global warming. We conducted a survey of 404 herdsmen households in a typical steppe region of Inner Mongolia, the province with the highest number of livestock in China. This study identifies different livestock management modes, and analyzes their carbon footprint and carbon efficiencies. The results show that: (1) according to source of household income, there are seven management modes, including small livestock breeding-oriented, large livestock breeding-oriented, mixed small/large livestock breeding, grassland byproduct-oriented, pasture leasing-oriented, non-livestockoriented, and mixed with breeding and non-livestock. The dominant management mode in this typical steppe region of Inner Mongolia was small livestock breeding-oriented. (2) Among the seven management modes, mixed with breeding, and non-livestock management, had the highest household carbon footprint (7.22 t CO2), and also the highest household income (208,500 Chinese Yuan), while grassland byproduct-oriented management had the lowest household carbon footprint (5.07 t CO2), and pasture leasing-oriented management had the lowest household income (71,000 Chinese Yuan). (3) Based on the Gini coefficient, the distribution of household carbon footprint among the seven management modes was relatively equal (0.26), but the direct carbon footprint showed the greatest inequality among these management modes (0.38). (4) The seven management modes showed significant differences in carbon efficiency, with grassland byproduct-oriented management, and mixed small/large livestock breeding management, ranked in the top two, while pasture leasing-oriented management had the lowest carbon efficiency. We identified grassland byproduct-oriented management, and mixed small/large livestock breeding management, as the two most optimal management modes. Although the pasture leasingoriented management mode exhibited a lower household carbon footprint, it also had the lowest carbon efficiency, and so its adoption should be cautious. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Livestock Carbon footprint Carbon efficiency Income
1. Introduction Livestock system occupies 30% of the Earth's ice-free land surface, contributes 40% of global agricultural gross domestic product, and provides important protein and other nutritional elements to no less than 1.3 billion people (Herrero et al., 2013). Meanwhile,
* Corresponding author. Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China. E-mail address: [email protected] (Y. Zhao). https://doi.org/10.1016/j.jclepro.2019.05.205 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
greenhouse gas emissions caused by livestock system account for 19% of the global total (Reisinger and Clark, 2018). Considering that global warming, which is mainly caused by greenhouse gas emissions, has become one of the most serious environmental problems, it is of great importance to explore how to develop livestock systems to reduce their greenhouse gas emissions (Persson et al., 2015). The term “carbon footprint” refers to the total greenhouse gas emissions generated in the process of production and consumption by humans (Baiocchi et al., 2015; Salo et al., 2019). It can be used to evaluate the influence of human activities on the global greenhouse effect by quantitatively measuring carbon emissions at different
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scales, including household, regional, and national scales (Markaki et al., 2017; Minx et al., 2013; Penz et al., 2019). As the household is the basic unit of production and consumption, its management mode will directly affect the carbon footprint (Veeramani et al., 2017; Yan et al., 2015). Therefore, it is necessary to understand the household carbon footprint of different management modes, in order to reduce carbon emissions and achieve a low-carbon lifestyle (Kennedy et al., 2014; Robertson, 2016; Schanes et al., 2016). A series of studies have been conducted to explore the effects of different management modes on household carbon footprint, including agricultural production (Yan et al., 2015; Zhu et al., 2018), solid waste treatment (Deus et al., 2017), livestock production (Eldesouky et al., 2018; Luo et al., 2015) and dietary patterns (Veeramani et al., 2017; Xu et al., 2018). The impact of different management modes on the carbon footprint of livestock systems has also attracted widespread attention (Eldesouky et al., 2018; Herrero et al., 2013). On one hand, the carbon footprint of different livestock systems show significant variations. Patra (2017) found that the average carbon footprint of fresh milk production in India was highest for indigenous cattle, then goats, then buffaloes, and crossbred cows had the lowest footprint. In the Dehesa agroforestry systems of southwest Spain, beef farming is considered to have the lowest carbon footprint, followed by sheep farming for meat, and then farming of calves for sale (Eldesouky et al., 2018). On the other hand, there is also significant variation of the carbon footprint among different management modes of the same livestock system. Luo et al. (2015) revealed that an aggregated livestock management system is more effective than a household livestock management system in reducing greenhouse gas emissions. However, an organic beef production system showed a higher carbon footprint than a conventional beef production system in Italy (Buratti et al., 2017). These studies have shown that three crucial factors contribute to the variation of the carbon footprint of a livestock system in different management modes; these are enteric fermentation, forage use efficiency, and manure treatment (Eldesouky et al., 2018; Luo et al., 2015; Patra, 2017). Therefore, the need to develop a low greenhouse gas emission, highly efficient, “green management mode” livestock system for sustainable development has gradually become a consensus among national governments and various other organizations (Neto et al., 2018; Schanes et al., 2016). China is the largest greenhouse gas emitter in the world, producing about one third of the global total (Olivier et al., 2015). Livestock system makes up over 30% of the national agricultural gross domestic product, making China the biggest livestock producer in the world (Luo et al., 2015). To attain the global carbon emissions reduction target, which means that greenhouse gas emissions must be reduced by 70% by 2050 (Food and Agriculture Organization (FAO), 2016), it is necessary to urgently assess the carbon footprint of livestock systems with different management modes. There have been studies on the influence of different management modes on the household carbon footprint of livestock systems in agricultural areas and agro-pastoral transitional zones (Luo et al., 2015; Pan et al., 2016; Qu et al., 2013; Zhou et al., 2018). However, the impact of different management modes on household carbon footprint in pastoral areas is extremely unclear, as there has been little research on this. The Inner Mongolia Autonomous Region is the province with the greatest livestock number (almost all ruminants, such as cattle and sheep) in China. Considering that 80% of global livestock greenhouse gas emissions originate from ruminant farming systems (Persson et al., 2015), it is necessary to explore the household carbon footprint of different livestock management modes in Inner Mongolia. This study therefore surveys 404 herdsmen households in a typical steppe region of Inner Mongolia. Seven different management modes are identified
according to household income, and household carbon footprint and carbon efficiency under the different modes are then analyzed. We expect that our findings could provide scientific guidance regarding the development of low-carbon livestock production in the pastoral area of Inner Mongolia. 2. Material and methods 2.1. Study area The study was conducted in Xilinhot City, Inner Mongolia Autonomous Region, China (115180 -11760 E; 43 20 -44 520 N). The study area, which is in the center of the steppe region of Inner Mongolia, has been traditionally used as grazing land by Mongolian herdsmen for thousands of years. The region experiences a temperate, semi-arid climate, with mean annual precipitation of 295 mm, and mean annual temperature of 0.1 C. The landscape is dominated by high plains and hills, and largely covered by typical steppe vegetation on a chestnut soil. Xilinhot City was established in 1983. The population consists of 30 ethnic groups, with Mongolian being the most common at 28% (Xilinhot Bureau of Statistics, 2016). 2.2. Data collection 2.2.1. Household survey Face-to-face interviews with herdsmen were conducted in Beilike Ranch and Baoligen Town, which are representative regions of typical steppe in the Xilinhot city area. A systematic random geographic sampling method was used to select households to be surveyed. We choose the town government as the center, and carried out the survey in 8 different directions along the road. A herdsmen household was selected approximately every 3 km, and a total of 404 households were surveyed (Fig. 1). With 4280 herdsmen households in Xilinhot City, our sampling ratio was approximately 10%, which is sufficient to reflect the carbon footprint of a typical herdsmen household in the steppe (Qu et al., 2013). The face-to-face interviews with herdsmen were conducted during 24 days in June and July 2017. All the households agreed to take part in the survey. We determined the position of each household by GPS, then spent 2e3 h conducting the survey using a prepared questionnaire. The household survey was conducted by three groups simultaneously. Each group consisted of two project researchers and one local translator proficient in Mongolian and Mandarin. While one project researcher was in charge of recording, the other researcher together with the translator took responsibilities for interviewing. The survey covered information about the household, housing and other infrastructure (including covered folds and fences), the income of the household in 2016, and the daily expenditure of the household in 2016 (including daily living expenses, household construction expenses and transportation fees, etc.). Household income was divided into five types, as follows: income from breeding of small livestock; income from breeding of large livestock; income from marketing of grassland byproducts; income from leasing of pasture; and income not related to livestock. 2.2.2. Carbon footprint estimation The life cycle assessment (LCA) method was adopted to estimate the household's carbon footprint (Bin and Dowlatabadi, 2005). Household carbon footprint usually consist of both direct and indirect components. Carbon emissions incurred by illumination, heating and cooking are referred to as the direct household carbon footprint. Those incurred during production and transportation of non-energy products to satisfy the household basic needs are referred to as indirect household carbon footprint (Kennedy et al.,
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Fig. 1. Distribution of herdsmen households surveyed.
2014). The total household carbon footprint (CFTotal) is the sum of the direct (CFDirect) and indirect (CFIndirect) parts (Eq. (1)). CFDirect is the direct emissions from the herdsmen's energy consumption, calculated from the consumption of each energy type (Ni) and their CO2 emission factor (EFi) (Eq. (2)). The CO2 emission factors of the energy and products are shown in Table 1.
CFTotal ¼ CFDirect þ CFIndirect
(1)
CFDirect ¼
n X
Ni EFi
(2)
i¼1
CFIndirect ¼ CFClothes þ CFFood þ CFHouse þ CFTravel
(3)
CFIndirect refers to the indirect household carbon footprint, which mainly consists of the four parts given in Equation (Eq. (3)); i.e., the carbon footprint of clothing production and cleaning (CFClothes), food production and processing (CFFood), house construction
Table 1 CO2 emission factors and citation origins of the energy and products. Carbon footprint type
Category
CO2 Emission Factor
Citation origin
Direct household carbon footprint
Raw coal Cow dung Electricity Clothes Washing powder Grain Meat Milk Vegetable Vegetable oil Cement Iron and steel Sand Crushed stone Red brick Gasoline
2.530 kg CO2/kg 1.593 kg CO2/kg 0.896 kg CO2/KWh 6.420 kg CO2 each 0.720 kg CO2/kg 14.049 kg CO2/kg 22.000 kg CO2/kg 1.400 kg CO2/kg 0.330 kg CO2/kg 8.820 kg CO2/kg 0.800 kg CO2/kg 1.920 kg CO2/kg 1.080 kg CO2/kg 0.610 kg CO2/kg 0.450 kg CO2/kg 2.361 kg CO2/kg
(Eggleston et al., 2006) (Eggleston et al., 2006) (Liu and Zai, 2014) (Guo et al., 2015) (Guo et al., 2015) (Wu et al., 2012) Yan et al. (2015) Yan et al. (2015) Yan et al. (2015) (Wu et al., 2012) (Zhu, 2012) ( Zhu, 2012) (Zhu, 2012) ( Zhu, 2012) (Zhu, 2012) (Eggleston et al., 2006)
Clothing production and cleaning Food production and processing
House construction
Travel
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(CFHouse), and travel (CFTravel). The formulas for calculating various carbon footprint are listed as Equations (4)e(7).
CFClothes ¼ NClothes EFClothes þ NDetergent EFDetergent
(4)
where NClothes represents the quantity of clothes bought by each household in 2016, NDetergent is the amount of detergent used, and EFClothes and EFDetergent are the respective CO2 emission factors of clothes and detergent.
CFFood ¼
n X
Nj EFj
(5)
j¼1
where Nj is consumption of food category j (including grains, meat, milk, vegetables, and vegetable oil), and EFj is the corresponding CO2 emission factor. Considering that herdsmen in this area primarily eat mutton, its CO2 emission factor is adopted for meat. The construction of a herdsmen's house in the studied region has four principal parts: a covered fold, a fence, a residence on the pasture (typically a brick structure) and a residential building in the city (typically a steel structure). According to China's Construction Specification, the service life of an urban residential building, pasturing residence, covered fold, and fence should be 70, 40, 20, and 10 y, respectively. An urban residential building requires 236 kg cement, 38.88 kg steel, 145 kg sand, and 343.7 kg crushed stone per m2. A pasturing residence requires 332.8 kg red bricks, 68 kg sand, and 11 kg cement per m2. A covered fold requires 66.6 kg red bricks, 13.5 kg sand, and 2.5 kg cement per m2, and 1.19 kg steel is needed to construct 1 m of fence. The carbon footprint of house construction was calculated using Equation (6).
CFHouse ¼
4 X n X
, Ml Areak EFl
Yeark
(6)
k¼1 l¼1
where, k refers to the four aspects of house construction (i.e., urban residential building, pasturing residence, covered fold, and fence), Ml refers to the consumption of material l during construction of one unit area, Areak the construction area of the house; EFl refers to the CO2 emission factor of material l, and Yeark the service life of the house. The household carbon footprint of travel involves carbon emissions incurred by going into town to purchase daily supplies and participate in social activities. The overall transportation costs of household in 2016 were investigated using questionnaires, and the costs were converted into gasoline usage uniformly. According to the 2016 gasoline price in the Xilinhot City (6.75 Chinese Yuan per liter), the carbon footprint of travel was calculated using Equation (7).
CFTravel ¼ Fm =6:75 EFgasoline
(7)
where, Fm is the transportation cost of a household in 2016, and EFgasoline represents the CO2 emission factor of gasoline. 2.3. Data analysis 2.3.1. Classification of household management modes There are five main subsistence activities of the herdsmen households in this region, including breeding small livestock (sheep), breeding large livestock (cattle and horses), marketing grassland byproducts (hay, dairy products, furs, etc.), pasture leasing, and non-livestock activities (out-migration for work, developing ecotourism, doing business, etc.). According to these five subsistence activities, we also divided each household income
into the corresponding five categories. With household income as the criterion (van den Berg, 2010), 404 herdsmen households were classified into different management modes using principal component analysis (PCA). Then, descriptive statistics were used to illustrate the household characteristics of the different management modes. A one-way analysis of variance was used to analyze the differences of household income among the different management modes. 2.3.2. Household carbon footprint characteristics of different management modes Firstly, descriptive statistics were employed to identify the household carbon footprint composition of the different management modes. Secondly, one-way analysis of variance was used to explore the difference of household carbon footprint among the different management modes. In order to evaluate the inequality of household carbon footprint among the different management modes, Gini coefficients were calculated for the household carbon footprint and for each of its five parts (CFDirect, CFClothes, CFFood, CFHouse and CFTravel). The Gini coefficient is generally divided into five levels: lower than 0.2, between 0.2 and 0.3, between 0.3 and 0.4, between 0.4 and 0.6, and over 0.6; these correspond to five grades of inequality: extreme equality, relative equality, inequality, much inequality and extreme inequality (Sadras and Bongiovanni, 2004). 2.3.3. Carbon efficiency of different household management modes Carbon efficiency refers to the economic value generated per unit of carbon emissions. It can be used to evaluate the optimization level of different household management modes. The carbon efficiency of every household was calculated using Equation (8).
Ce ¼ Ihousehold =CFTotal
(8)
where, Ce stands for carbon efficiency of the household, Ihousehold represents the income of the household, and CFTotal means the total household carbon footprint. On the basis of the calculated carbon efficiency, a one-way analysis of variance was employed to explore the difference among different management modes. 3. Results 3.1. Classification of herdsmen household management modes The cumulative contribution rate of the first two axes of the PCA is 74.8% (more than 0.7), so the PCA result can be used to classify the different management modes. Based on the PCA, 404 herdsmen households can be divided into seven management modes (Fig. 2): small livestock breeding-oriented (I), large livestock breedingoriented (II), mixed small/large livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), nonlivestock-oriented (Ⅵ), and mixed with breeding and nonlivestock (Ⅶ). The small livestock breeding-oriented management mode, with 175 households (Table 2), accounted for 43.32% of the total households. This kind of household management mode was dominant in pastoral areas of the Inner Mongolia grassland. The mixed small/large livestock breeding management mode showed the largest number of household members (3.25), and the highest labor population ratio (0.91). The pasture leasing-oriented management mode had the oldest household head (53.8 y) and a relatively lower labor population ratio (0.71). The grassland byproductoriented management mode had the highest level of education (2.96). There were significant differences of household income among the seven management modes. The top three management modes for income were mixed with breeding and non-livestock, large livestock breeding-oriented, and mixed small/large livestock
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Fig. 2. Classification of herdsman household management modes based on PCA. The hollow gray circle means every herdsman household. The red line represents five categories household income including income of selling small livestock, income of selling big livestock, income of selling grassland byproduct, income of leasing pasture and income of nonlivestock. Each blue oval is a kind of household management mode. They are labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing seven different household management mode as follows: small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasingoriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
Table 2 Household characteristics of different management modes. Management mode
Subsistence activity
Number of herdsman household
Number of household member
Labor population ratio
Age of household head
Educational level of household head
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproductsoriented Pasture leasingoriented Non- livestock -oriented Mixed with breeding and non- livestock
Traditional livestock management mode mainly breeding sheep and goats Traditional livestock management mode mainly breeding cattle and horse Traditional livestock management mode mixed breeding sheep, goats, cattle, and horse Non-traditional livestock management mode mainly selling hay, dairy product, and fur
175
3.02
0.83
47.56
2.84
96
2.86
0.76
48.72
2.84
20
3.25
0.91
46.55
2.6
54
2.50
0.87
46.76
2.96
Non-traditional livestock management mode mainly leasing 26 pasture Non- livestock management mode such as out-migration for 22 work, developing ecotourism, doing business, etc. Simultaneously conducting livestock breeding and non11 livestock activities
2.69
0.71
53.38
2.69
3.23
0.80
51.13
2.90
2.64
0.50
54.36
2.91
Note: The education level of the household head is divided into four categories including: Illiteracy, Elementary education, Secondary education, Higher education, labeled as 1, 2, 3, 4, respectively. Labor means household member at working age (that is, at S18 and & 60 years old).
breeding (Fig. 3). The highest household income, for mixed management with breeding and non-livestock (208,500 Chinese Yuan), was 2.97 times the lowest household income, for pasture leasingoriented management (71,000 Chinese Yuan).
3.2. Household carbon footprint of different management modes The household carbon footprint of the seven management modes were between 5.07 and 7.22 t CO2, and mixed with breeding
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management modes was relatively equal (0.26), but the direct carbon footprint showed the greatest inequality among the seven management modes (0.38). 3.3. Household carbon efficiency of different management modes
Fig. 3. Differences of household income among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/ big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
and non-livestock management had the highest footprint. The two non-traditional livestock management modes, including pasture leasing-oriented and grassland byproducts-oriented, had the two lowest household carbon footprint (Fig. 4). However, the composition of household carbon footprint among the seven management modes showed a similar pattern. The direct carbon footprint, the food production and processing carbon footprint, and the housing construction carbon footprint ranked in the top three. Half of the household carbon footprint consisted of the direct carbon footprint (Table 3). Based on the Gini coefficient (Table 4), it was found that the distribution of household carbon footprint among the seven
Fig. 4. Differences of household carbon footprint among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and nonlivestock (Ⅶ), respectively.
There were significant differences among the seven management modes in terms of household carbon efficiency (Fig. 5). The highest carbon efficiency was attained by the grassland byproducts-oriented management mode (43,410 Chinese Yuan/t CO2), which was 2.99 times the lowest carbon efficiency, for the pasture leasing-oriented management mode (14,510 Chinese Yuan/ t CO2). The carbon efficiency of the grassland byproducts-oriented management mode was obviously higher than that of the other six modes. In terms of the three traditional livestock management modes, including mixed small/large livestock breeding, small livestock breeding-oriented, and large livestock breeding-oriented, the carbon efficiency of the mixed small/large livestock breeding mode was highest (Fig. 5). 4. Discussion Nomadism was the earliest grassland management mode in the Inner Mongolia grassland, and has been practiced for thousands of years. With the liberation of the Inner Mongolia Autonomous Region in 1947, the grassland management mode gradually changed from primitive nomadism to a half-sedentary management mode, which means that herdsmen inhabit and conduct nomadic management in a certain area. In the early 1980s, with the development of China's Reform and Opening-up Policy and market economy, the general land management system of “Double Power and One System” was implemented in pastoral areas, and the completely sedentary management mode began (Li et al., 2007; Wu et al., 2015). In order to improve grassland utilization efficiency and implement intensive management, the pasture leasing management mode emerged in the last decade (Li et al., 2018). In response to grassland degradation, a series of non-livestock management modes, such as out-migration for work, development of ecotourism, doing business, etc. were gradually known and accepted by the herdsmen (Fan et al., 2015). Nowadays, the Inner Mongolia pastoral area presents diverse management modes, dominated by the traditional livestock management mode, and supplemented by non-traditional livestock management modes and non-livestock management modes (Fig. 2 and Table 2). What kind of household management mode a herdsmen adopts is often strongly influenced by the labor force and age (Nogueira et al., 2016; Qu et al., 2013). Our study also found that traditional livestock management modes often have a high household member and labor population ratio, to meet the needs of heavy grazing work (Table 2). However, the number of household members, labor population ratio, and age of the household head for the pasture leasing-oriented management mode were 2.69, 0.71 and 53.38, respectively (Table 2). Due to the lack of a sufficient labor force to carry out traditional livestock management, the pasture leasingoriented management mode was necessarily adopted. Educational level is also a crucial factor in determining household management mode (Sovacool et al., 2018). Table 2 shows that the education level of the household head was highest for the grassland byproductoriented management mode. There are probably two main reasons for this. On the one hand, a high education level makes it easier to accept new technologies. On the other hand, it also helps with obtaining information needed for selling hay and dairy products. Many studies have found that when household income is lower, the household carbon footprint is mainly used for necessities such
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Table 3 Proportion of five components of household carbon footprint of seven different management modes (%). Management mode
CFDirect
CFClothes
CFFood
CFHouse
CFTravel
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproducts-oriented Pasture leasing-oriented Non- livestock -oriented Mixed with breeding and non- livestock
53.48 50.62 47.85 50.86 50.18 45.14 60.33
0.59 0.59 0.61 0.63 0.62 0.76 0.61
20.51 20.56 21.55 24.22 23.84 24.61 17.38
16.78 19.65 21.05 15.35 18.39 20.91 14.15
8.64 8.59 8.94 8.93 6.97 8.58 7.53
Note: CFDirect means direct carbon footprint; CFClothes means clothing production and cleaning carbon footprint; CFFood means food production and processing carbon footprint; CFHouse means house construction carbon footprint; CFTravel means travel carbon footprint.
Table 4 Five components of carbon footprint (t co2) and Gini coefficient of seven household management modes. Management mode
CFDirect
CFClothes
CFFood
CFHouse
CFTravel
CFTotal
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproducts-oriented Pasture leasing-oriented Non- livestock -oriented Mixed with breeding and non- livestock Gini coefficient
3.72 3.49 2.93 2.58 2.66 2.99 4.35 0.38
0.04 0.04 0.04 0.03 0.03 0.05 0.04 0.32
1.43 1.42 1.41 1.23 1.26 1.63 1.25 0.29
1.17 1.36 1.37 0.78 0.97 1.39 1.02 0.31
0.6 0.59 0.78 0.45 0.37 0.57 0.54 0.37
6.96 6.90 6.53 5.07 5.29 6.63 7.22 0.26
Note: CFDirect means direct carbon footprint; CFClothes means clothing production and cleaning carbon footprint; CFFood means food production and processing carbon footprint; CFHouse means house construction carbon footprint; CFTravel means travel carbon footprint. CFTotal means total household carbon footprint.
Fig. 5. Differences of carbon efficiency among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/ big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
as food, fuels and the house itself; when household income is higher, the household carbon footprint is also used to buy a car and for travelling etc., to improve the quality of life (Majid et al., 2014; Sommer and Kratena, 2017). Our study also supports this viewpoint. As the per capita disposable income of Inner Mongolia is lower than the national average (National Bureas of Statistics), household consumption is mainly concentrated on basic necessities such as fuel, food and housing. This leads to the direct carbon footprint, food production and processing carbon footprint, and
housing construction carbon footprint being ranked in the top three (Table 4). Meanwhile, the Gini coefficient shows that the greatest inequality among the seven management modes was in the direct household carbon footprint (Table 4). The main reason for this may be differences in energy utilization. In the pastoral areas of Inner Mongolia, there are three main energy sources: raw coal, electricity, and cow dung (Zhu et al., 2012). Because herdsmen households are often distributed very sparsely, the power grid can only cover a limited area. The herdsmen households in these limited areas mainly use electricity for energy, while those of other areas have to use raw coal or cow dung. The differences in CO2 emissions among raw coal, electricity and cow dung lead to the greatest inequality in the direct carbon footprint among the seven management modes. Despite the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode generating moderate levels of income (Fig. 2), the carbon efficiencies of these two modes rank in the top two (Fig. 4). Therefore, these are considered to be the two most optimal management modes for this typical steppe region of Inner Mongolia. Many studies have confirmed that resource allocation and utilization efficiency are key factors in determining carbon efficiency (Eldesouky et al., 2018; Neto et al., 2018; Pan et al., 2016). The highest carbon efficiency of the grassland byproduct-oriented management mode may be due to that mode having the lowest carbon emissions (Fig. 4). This kind of management mainly involves the production and sale of hay, with almost no livestock. Thus the very low consumption for livestock production leads to a significant reduction of the household carbon footprint, and the highest carbon efficiency. For the mixed small/large livestock breeding management mode, since large livestock (cattle, horses) and small livestock (sheep) have different feeding preferences (Cuchillo-Hilario et al., 2018), the mixed small/large livestock breeding can make full use of the grassland resources, and further result in higher carbon efficiency (Wang et al., 2019). Zhu et al. (2018) recommended the strengthening of land use rights transfers, to mitigate negative impacts on the environment. However, our study found that the pasture leasing-oriented management mode had the lowest carbon
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efficiency among the seven management modes (Fig. 5). This may be connected with this management mode having the lowest economic efficiency (Fig. 3). Many studies have found that land leasing can improve overall economic efficiency, especially the economic efficiency of the renter, which will generally increase significantly; though the economic efficiency of the leaser often shows no significant change, or even a decrease (Chamberlin and Ricker-Gilbert, 2016; Pender and Fafchamps, 2006). Therefore, the adoption of the pasture leasing-oriented management mode should be generally cautious. Although the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode are considered to be the two optimal management modes in the typical steppe region of Inner Mongolia, their adoption is restricted by a series of factors, including educational level, labor force and others (Table 2). Therefore, in order to reduce carbon emissions and develop low-carbon livestock, local government intervention will be crucial. Firstly, our study found that small livestock (mainly sheep) breeding-oriented was the dominant management mode in this region (Table 2), so it is reasonable to improve the proportion of large livestock (mainly cattle). Although Inner Mongolia proposed a policy of “reducing sheep and increasing cattle” in 2016, its effect has not been good because of the limited traditional consciousness of the herders. The local government should therefore step up its efforts to promote the policy. Secondly, the herders’ traditional energy source is cattle and sheep manure, and the carbon efficiency of this method is very low. Considering the largest proportion of the direct carbon footprint (Table 3), it is reasonable to increase the use of electricity in this region. Many studies have found that technology has a large role in reducing carbon emissions, and is willingly adopted (Pan et al., 2016; Zhou et al., 2018). Increasing the power grid coverage as much as possible, and also promoting the use of wind and solar hybrid generators in remote pastoral households, will be very useful for reducing carbon emissions. In addition, traditional livestock feeding methods do not use any forage additives in this region, and some forage additives are very effective in reducing methane emissions (Luo et al., 2015). Therefore, it is possible to consider introducing forage additives to reduce carbon emissions. Finally, the method of selling livestock products in this region is relatively simple, with most being sold to small businessmen. The government should therefore cooperate with the herdsmen to build a number of distinctive livestock product brands around beef and lamb, and thus further expand their markets. This can achieve the goal of reducing the livestock number and carbon emissions without reducing the income of the herdsmen. 5. Conclusion It is of great importance to explore the carbon footprint of different livestock management modes, in order to reduce greenhouse gas emissions and mitigate global warming. Inner Mongolia has the most livestock of any province in China. Based on a survey of 404 herdsmen households in a typical steppe region of Inner Mongolia, we found that there are seven different management modes, and that the region is dominated by the small livestock breeding-oriented management mode. There were significant differences of household income, household carbon footprint and carbon efficiency among the seven management modes. In view of the lower carbon footprint and higher carbon efficiency, the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode are considered to be the two most optimal management modes in this region. Since it has the lowest household income and the lowest carbon efficiency, the adoption of the pasture leasing-oriented management mode should be cautious.
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Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Optimal herdsmen household management modes in a typical steppe region of Inner Mongolia, China Qing Zhang a, Yanyun Zhao a, b, * , Frank Yonghong Li a a Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China b Grassland Research Institute of Chinese Academy of Agricultural Sciences, Hohhot, 010021, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 December 2018 Received in revised form 28 April 2019 Accepted 18 May 2019 Available online 20 May 2019
Livestock systems account for 19% of total global greenhouse gas emissions, so it is of great importance to explore their carbon footprint under different management modes for reducing greenhouse gas emissions and mitigating global warming. We conducted a survey of 404 herdsmen households in a typical steppe region of Inner Mongolia, the province with the highest number of livestock in China. This study identifies different livestock management modes, and analyzes their carbon footprint and carbon efficiencies. The results show that: (1) according to source of household income, there are seven management modes, including small livestock breeding-oriented, large livestock breeding-oriented, mixed small/large livestock breeding, grassland byproduct-oriented, pasture leasing-oriented, non-livestockoriented, and mixed with breeding and non-livestock. The dominant management mode in this typical steppe region of Inner Mongolia was small livestock breeding-oriented. (2) Among the seven management modes, mixed with breeding, and non-livestock management, had the highest household carbon footprint (7.22 t CO2), and also the highest household income (208,500 Chinese Yuan), while grassland byproduct-oriented management had the lowest household carbon footprint (5.07 t CO2), and pasture leasing-oriented management had the lowest household income (71,000 Chinese Yuan). (3) Based on the Gini coefficient, the distribution of household carbon footprint among the seven management modes was relatively equal (0.26), but the direct carbon footprint showed the greatest inequality among these management modes (0.38). (4) The seven management modes showed significant differences in carbon efficiency, with grassland byproduct-oriented management, and mixed small/large livestock breeding management, ranked in the top two, while pasture leasing-oriented management had the lowest carbon efficiency. We identified grassland byproduct-oriented management, and mixed small/large livestock breeding management, as the two most optimal management modes. Although the pasture leasingoriented management mode exhibited a lower household carbon footprint, it also had the lowest carbon efficiency, and so its adoption should be cautious. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Livestock Carbon footprint Carbon efficiency Income
1. Introduction Livestock system occupies 30% of the Earth's ice-free land surface, contributes 40% of global agricultural gross domestic product, and provides important protein and other nutritional elements to no less than 1.3 billion people (Herrero et al., 2013). Meanwhile,
* Corresponding author. Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China. E-mail address: [email protected] (Y. Zhao). https://doi.org/10.1016/j.jclepro.2019.05.205 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
greenhouse gas emissions caused by livestock system account for 19% of the global total (Reisinger and Clark, 2018). Considering that global warming, which is mainly caused by greenhouse gas emissions, has become one of the most serious environmental problems, it is of great importance to explore how to develop livestock systems to reduce their greenhouse gas emissions (Persson et al., 2015). The term “carbon footprint” refers to the total greenhouse gas emissions generated in the process of production and consumption by humans (Baiocchi et al., 2015; Salo et al., 2019). It can be used to evaluate the influence of human activities on the global greenhouse effect by quantitatively measuring carbon emissions at different
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scales, including household, regional, and national scales (Markaki et al., 2017; Minx et al., 2013; Penz et al., 2019). As the household is the basic unit of production and consumption, its management mode will directly affect the carbon footprint (Veeramani et al., 2017; Yan et al., 2015). Therefore, it is necessary to understand the household carbon footprint of different management modes, in order to reduce carbon emissions and achieve a low-carbon lifestyle (Kennedy et al., 2014; Robertson, 2016; Schanes et al., 2016). A series of studies have been conducted to explore the effects of different management modes on household carbon footprint, including agricultural production (Yan et al., 2015; Zhu et al., 2018), solid waste treatment (Deus et al., 2017), livestock production (Eldesouky et al., 2018; Luo et al., 2015) and dietary patterns (Veeramani et al., 2017; Xu et al., 2018). The impact of different management modes on the carbon footprint of livestock systems has also attracted widespread attention (Eldesouky et al., 2018; Herrero et al., 2013). On one hand, the carbon footprint of different livestock systems show significant variations. Patra (2017) found that the average carbon footprint of fresh milk production in India was highest for indigenous cattle, then goats, then buffaloes, and crossbred cows had the lowest footprint. In the Dehesa agroforestry systems of southwest Spain, beef farming is considered to have the lowest carbon footprint, followed by sheep farming for meat, and then farming of calves for sale (Eldesouky et al., 2018). On the other hand, there is also significant variation of the carbon footprint among different management modes of the same livestock system. Luo et al. (2015) revealed that an aggregated livestock management system is more effective than a household livestock management system in reducing greenhouse gas emissions. However, an organic beef production system showed a higher carbon footprint than a conventional beef production system in Italy (Buratti et al., 2017). These studies have shown that three crucial factors contribute to the variation of the carbon footprint of a livestock system in different management modes; these are enteric fermentation, forage use efficiency, and manure treatment (Eldesouky et al., 2018; Luo et al., 2015; Patra, 2017). Therefore, the need to develop a low greenhouse gas emission, highly efficient, “green management mode” livestock system for sustainable development has gradually become a consensus among national governments and various other organizations (Neto et al., 2018; Schanes et al., 2016). China is the largest greenhouse gas emitter in the world, producing about one third of the global total (Olivier et al., 2015). Livestock system makes up over 30% of the national agricultural gross domestic product, making China the biggest livestock producer in the world (Luo et al., 2015). To attain the global carbon emissions reduction target, which means that greenhouse gas emissions must be reduced by 70% by 2050 (Food and Agriculture Organization (FAO), 2016), it is necessary to urgently assess the carbon footprint of livestock systems with different management modes. There have been studies on the influence of different management modes on the household carbon footprint of livestock systems in agricultural areas and agro-pastoral transitional zones (Luo et al., 2015; Pan et al., 2016; Qu et al., 2013; Zhou et al., 2018). However, the impact of different management modes on household carbon footprint in pastoral areas is extremely unclear, as there has been little research on this. The Inner Mongolia Autonomous Region is the province with the greatest livestock number (almost all ruminants, such as cattle and sheep) in China. Considering that 80% of global livestock greenhouse gas emissions originate from ruminant farming systems (Persson et al., 2015), it is necessary to explore the household carbon footprint of different livestock management modes in Inner Mongolia. This study therefore surveys 404 herdsmen households in a typical steppe region of Inner Mongolia. Seven different management modes are identified
according to household income, and household carbon footprint and carbon efficiency under the different modes are then analyzed. We expect that our findings could provide scientific guidance regarding the development of low-carbon livestock production in the pastoral area of Inner Mongolia. 2. Material and methods 2.1. Study area The study was conducted in Xilinhot City, Inner Mongolia Autonomous Region, China (115180 -11760 E; 43 20 -44 520 N). The study area, which is in the center of the steppe region of Inner Mongolia, has been traditionally used as grazing land by Mongolian herdsmen for thousands of years. The region experiences a temperate, semi-arid climate, with mean annual precipitation of 295 mm, and mean annual temperature of 0.1 C. The landscape is dominated by high plains and hills, and largely covered by typical steppe vegetation on a chestnut soil. Xilinhot City was established in 1983. The population consists of 30 ethnic groups, with Mongolian being the most common at 28% (Xilinhot Bureau of Statistics, 2016). 2.2. Data collection 2.2.1. Household survey Face-to-face interviews with herdsmen were conducted in Beilike Ranch and Baoligen Town, which are representative regions of typical steppe in the Xilinhot city area. A systematic random geographic sampling method was used to select households to be surveyed. We choose the town government as the center, and carried out the survey in 8 different directions along the road. A herdsmen household was selected approximately every 3 km, and a total of 404 households were surveyed (Fig. 1). With 4280 herdsmen households in Xilinhot City, our sampling ratio was approximately 10%, which is sufficient to reflect the carbon footprint of a typical herdsmen household in the steppe (Qu et al., 2013). The face-to-face interviews with herdsmen were conducted during 24 days in June and July 2017. All the households agreed to take part in the survey. We determined the position of each household by GPS, then spent 2e3 h conducting the survey using a prepared questionnaire. The household survey was conducted by three groups simultaneously. Each group consisted of two project researchers and one local translator proficient in Mongolian and Mandarin. While one project researcher was in charge of recording, the other researcher together with the translator took responsibilities for interviewing. The survey covered information about the household, housing and other infrastructure (including covered folds and fences), the income of the household in 2016, and the daily expenditure of the household in 2016 (including daily living expenses, household construction expenses and transportation fees, etc.). Household income was divided into five types, as follows: income from breeding of small livestock; income from breeding of large livestock; income from marketing of grassland byproducts; income from leasing of pasture; and income not related to livestock. 2.2.2. Carbon footprint estimation The life cycle assessment (LCA) method was adopted to estimate the household's carbon footprint (Bin and Dowlatabadi, 2005). Household carbon footprint usually consist of both direct and indirect components. Carbon emissions incurred by illumination, heating and cooking are referred to as the direct household carbon footprint. Those incurred during production and transportation of non-energy products to satisfy the household basic needs are referred to as indirect household carbon footprint (Kennedy et al.,
Q. Zhang et al. / Journal of Cleaner Production 231 (2019) 1e9
3
Fig. 1. Distribution of herdsmen households surveyed.
2014). The total household carbon footprint (CFTotal) is the sum of the direct (CFDirect) and indirect (CFIndirect) parts (Eq. (1)). CFDirect is the direct emissions from the herdsmen's energy consumption, calculated from the consumption of each energy type (Ni) and their CO2 emission factor (EFi) (Eq. (2)). The CO2 emission factors of the energy and products are shown in Table 1.
CFTotal ¼ CFDirect þ CFIndirect
(1)
CFDirect ¼
n X
Ni EFi
(2)
i¼1
CFIndirect ¼ CFClothes þ CFFood þ CFHouse þ CFTravel
(3)
CFIndirect refers to the indirect household carbon footprint, which mainly consists of the four parts given in Equation (Eq. (3)); i.e., the carbon footprint of clothing production and cleaning (CFClothes), food production and processing (CFFood), house construction
Table 1 CO2 emission factors and citation origins of the energy and products. Carbon footprint type
Category
CO2 Emission Factor
Citation origin
Direct household carbon footprint
Raw coal Cow dung Electricity Clothes Washing powder Grain Meat Milk Vegetable Vegetable oil Cement Iron and steel Sand Crushed stone Red brick Gasoline
2.530 kg CO2/kg 1.593 kg CO2/kg 0.896 kg CO2/KWh 6.420 kg CO2 each 0.720 kg CO2/kg 14.049 kg CO2/kg 22.000 kg CO2/kg 1.400 kg CO2/kg 0.330 kg CO2/kg 8.820 kg CO2/kg 0.800 kg CO2/kg 1.920 kg CO2/kg 1.080 kg CO2/kg 0.610 kg CO2/kg 0.450 kg CO2/kg 2.361 kg CO2/kg
(Eggleston et al., 2006) (Eggleston et al., 2006) (Liu and Zai, 2014) (Guo et al., 2015) (Guo et al., 2015) (Wu et al., 2012) Yan et al. (2015) Yan et al. (2015) Yan et al. (2015) (Wu et al., 2012) (Zhu, 2012) ( Zhu, 2012) (Zhu, 2012) ( Zhu, 2012) (Zhu, 2012) (Eggleston et al., 2006)
Clothing production and cleaning Food production and processing
House construction
Travel
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(CFHouse), and travel (CFTravel). The formulas for calculating various carbon footprint are listed as Equations (4)e(7).
CFClothes ¼ NClothes EFClothes þ NDetergent EFDetergent
(4)
where NClothes represents the quantity of clothes bought by each household in 2016, NDetergent is the amount of detergent used, and EFClothes and EFDetergent are the respective CO2 emission factors of clothes and detergent.
CFFood ¼
n X
Nj EFj
(5)
j¼1
where Nj is consumption of food category j (including grains, meat, milk, vegetables, and vegetable oil), and EFj is the corresponding CO2 emission factor. Considering that herdsmen in this area primarily eat mutton, its CO2 emission factor is adopted for meat. The construction of a herdsmen's house in the studied region has four principal parts: a covered fold, a fence, a residence on the pasture (typically a brick structure) and a residential building in the city (typically a steel structure). According to China's Construction Specification, the service life of an urban residential building, pasturing residence, covered fold, and fence should be 70, 40, 20, and 10 y, respectively. An urban residential building requires 236 kg cement, 38.88 kg steel, 145 kg sand, and 343.7 kg crushed stone per m2. A pasturing residence requires 332.8 kg red bricks, 68 kg sand, and 11 kg cement per m2. A covered fold requires 66.6 kg red bricks, 13.5 kg sand, and 2.5 kg cement per m2, and 1.19 kg steel is needed to construct 1 m of fence. The carbon footprint of house construction was calculated using Equation (6).
CFHouse ¼
4 X n X
, Ml Areak EFl
Yeark
(6)
k¼1 l¼1
where, k refers to the four aspects of house construction (i.e., urban residential building, pasturing residence, covered fold, and fence), Ml refers to the consumption of material l during construction of one unit area, Areak the construction area of the house; EFl refers to the CO2 emission factor of material l, and Yeark the service life of the house. The household carbon footprint of travel involves carbon emissions incurred by going into town to purchase daily supplies and participate in social activities. The overall transportation costs of household in 2016 were investigated using questionnaires, and the costs were converted into gasoline usage uniformly. According to the 2016 gasoline price in the Xilinhot City (6.75 Chinese Yuan per liter), the carbon footprint of travel was calculated using Equation (7).
CFTravel ¼ Fm =6:75 EFgasoline
(7)
where, Fm is the transportation cost of a household in 2016, and EFgasoline represents the CO2 emission factor of gasoline. 2.3. Data analysis 2.3.1. Classification of household management modes There are five main subsistence activities of the herdsmen households in this region, including breeding small livestock (sheep), breeding large livestock (cattle and horses), marketing grassland byproducts (hay, dairy products, furs, etc.), pasture leasing, and non-livestock activities (out-migration for work, developing ecotourism, doing business, etc.). According to these five subsistence activities, we also divided each household income
into the corresponding five categories. With household income as the criterion (van den Berg, 2010), 404 herdsmen households were classified into different management modes using principal component analysis (PCA). Then, descriptive statistics were used to illustrate the household characteristics of the different management modes. A one-way analysis of variance was used to analyze the differences of household income among the different management modes. 2.3.2. Household carbon footprint characteristics of different management modes Firstly, descriptive statistics were employed to identify the household carbon footprint composition of the different management modes. Secondly, one-way analysis of variance was used to explore the difference of household carbon footprint among the different management modes. In order to evaluate the inequality of household carbon footprint among the different management modes, Gini coefficients were calculated for the household carbon footprint and for each of its five parts (CFDirect, CFClothes, CFFood, CFHouse and CFTravel). The Gini coefficient is generally divided into five levels: lower than 0.2, between 0.2 and 0.3, between 0.3 and 0.4, between 0.4 and 0.6, and over 0.6; these correspond to five grades of inequality: extreme equality, relative equality, inequality, much inequality and extreme inequality (Sadras and Bongiovanni, 2004). 2.3.3. Carbon efficiency of different household management modes Carbon efficiency refers to the economic value generated per unit of carbon emissions. It can be used to evaluate the optimization level of different household management modes. The carbon efficiency of every household was calculated using Equation (8).
Ce ¼ Ihousehold =CFTotal
(8)
where, Ce stands for carbon efficiency of the household, Ihousehold represents the income of the household, and CFTotal means the total household carbon footprint. On the basis of the calculated carbon efficiency, a one-way analysis of variance was employed to explore the difference among different management modes. 3. Results 3.1. Classification of herdsmen household management modes The cumulative contribution rate of the first two axes of the PCA is 74.8% (more than 0.7), so the PCA result can be used to classify the different management modes. Based on the PCA, 404 herdsmen households can be divided into seven management modes (Fig. 2): small livestock breeding-oriented (I), large livestock breedingoriented (II), mixed small/large livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), nonlivestock-oriented (Ⅵ), and mixed with breeding and nonlivestock (Ⅶ). The small livestock breeding-oriented management mode, with 175 households (Table 2), accounted for 43.32% of the total households. This kind of household management mode was dominant in pastoral areas of the Inner Mongolia grassland. The mixed small/large livestock breeding management mode showed the largest number of household members (3.25), and the highest labor population ratio (0.91). The pasture leasing-oriented management mode had the oldest household head (53.8 y) and a relatively lower labor population ratio (0.71). The grassland byproductoriented management mode had the highest level of education (2.96). There were significant differences of household income among the seven management modes. The top three management modes for income were mixed with breeding and non-livestock, large livestock breeding-oriented, and mixed small/large livestock
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5
Fig. 2. Classification of herdsman household management modes based on PCA. The hollow gray circle means every herdsman household. The red line represents five categories household income including income of selling small livestock, income of selling big livestock, income of selling grassland byproduct, income of leasing pasture and income of nonlivestock. Each blue oval is a kind of household management mode. They are labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing seven different household management mode as follows: small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasingoriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
Table 2 Household characteristics of different management modes. Management mode
Subsistence activity
Number of herdsman household
Number of household member
Labor population ratio
Age of household head
Educational level of household head
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproductsoriented Pasture leasingoriented Non- livestock -oriented Mixed with breeding and non- livestock
Traditional livestock management mode mainly breeding sheep and goats Traditional livestock management mode mainly breeding cattle and horse Traditional livestock management mode mixed breeding sheep, goats, cattle, and horse Non-traditional livestock management mode mainly selling hay, dairy product, and fur
175
3.02
0.83
47.56
2.84
96
2.86
0.76
48.72
2.84
20
3.25
0.91
46.55
2.6
54
2.50
0.87
46.76
2.96
Non-traditional livestock management mode mainly leasing 26 pasture Non- livestock management mode such as out-migration for 22 work, developing ecotourism, doing business, etc. Simultaneously conducting livestock breeding and non11 livestock activities
2.69
0.71
53.38
2.69
3.23
0.80
51.13
2.90
2.64
0.50
54.36
2.91
Note: The education level of the household head is divided into four categories including: Illiteracy, Elementary education, Secondary education, Higher education, labeled as 1, 2, 3, 4, respectively. Labor means household member at working age (that is, at S18 and & 60 years old).
breeding (Fig. 3). The highest household income, for mixed management with breeding and non-livestock (208,500 Chinese Yuan), was 2.97 times the lowest household income, for pasture leasingoriented management (71,000 Chinese Yuan).
3.2. Household carbon footprint of different management modes The household carbon footprint of the seven management modes were between 5.07 and 7.22 t CO2, and mixed with breeding
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management modes was relatively equal (0.26), but the direct carbon footprint showed the greatest inequality among the seven management modes (0.38). 3.3. Household carbon efficiency of different management modes
Fig. 3. Differences of household income among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/ big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
and non-livestock management had the highest footprint. The two non-traditional livestock management modes, including pasture leasing-oriented and grassland byproducts-oriented, had the two lowest household carbon footprint (Fig. 4). However, the composition of household carbon footprint among the seven management modes showed a similar pattern. The direct carbon footprint, the food production and processing carbon footprint, and the housing construction carbon footprint ranked in the top three. Half of the household carbon footprint consisted of the direct carbon footprint (Table 3). Based on the Gini coefficient (Table 4), it was found that the distribution of household carbon footprint among the seven
Fig. 4. Differences of household carbon footprint among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and nonlivestock (Ⅶ), respectively.
There were significant differences among the seven management modes in terms of household carbon efficiency (Fig. 5). The highest carbon efficiency was attained by the grassland byproducts-oriented management mode (43,410 Chinese Yuan/t CO2), which was 2.99 times the lowest carbon efficiency, for the pasture leasing-oriented management mode (14,510 Chinese Yuan/ t CO2). The carbon efficiency of the grassland byproducts-oriented management mode was obviously higher than that of the other six modes. In terms of the three traditional livestock management modes, including mixed small/large livestock breeding, small livestock breeding-oriented, and large livestock breeding-oriented, the carbon efficiency of the mixed small/large livestock breeding mode was highest (Fig. 5). 4. Discussion Nomadism was the earliest grassland management mode in the Inner Mongolia grassland, and has been practiced for thousands of years. With the liberation of the Inner Mongolia Autonomous Region in 1947, the grassland management mode gradually changed from primitive nomadism to a half-sedentary management mode, which means that herdsmen inhabit and conduct nomadic management in a certain area. In the early 1980s, with the development of China's Reform and Opening-up Policy and market economy, the general land management system of “Double Power and One System” was implemented in pastoral areas, and the completely sedentary management mode began (Li et al., 2007; Wu et al., 2015). In order to improve grassland utilization efficiency and implement intensive management, the pasture leasing management mode emerged in the last decade (Li et al., 2018). In response to grassland degradation, a series of non-livestock management modes, such as out-migration for work, development of ecotourism, doing business, etc. were gradually known and accepted by the herdsmen (Fan et al., 2015). Nowadays, the Inner Mongolia pastoral area presents diverse management modes, dominated by the traditional livestock management mode, and supplemented by non-traditional livestock management modes and non-livestock management modes (Fig. 2 and Table 2). What kind of household management mode a herdsmen adopts is often strongly influenced by the labor force and age (Nogueira et al., 2016; Qu et al., 2013). Our study also found that traditional livestock management modes often have a high household member and labor population ratio, to meet the needs of heavy grazing work (Table 2). However, the number of household members, labor population ratio, and age of the household head for the pasture leasing-oriented management mode were 2.69, 0.71 and 53.38, respectively (Table 2). Due to the lack of a sufficient labor force to carry out traditional livestock management, the pasture leasingoriented management mode was necessarily adopted. Educational level is also a crucial factor in determining household management mode (Sovacool et al., 2018). Table 2 shows that the education level of the household head was highest for the grassland byproductoriented management mode. There are probably two main reasons for this. On the one hand, a high education level makes it easier to accept new technologies. On the other hand, it also helps with obtaining information needed for selling hay and dairy products. Many studies have found that when household income is lower, the household carbon footprint is mainly used for necessities such
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Table 3 Proportion of five components of household carbon footprint of seven different management modes (%). Management mode
CFDirect
CFClothes
CFFood
CFHouse
CFTravel
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproducts-oriented Pasture leasing-oriented Non- livestock -oriented Mixed with breeding and non- livestock
53.48 50.62 47.85 50.86 50.18 45.14 60.33
0.59 0.59 0.61 0.63 0.62 0.76 0.61
20.51 20.56 21.55 24.22 23.84 24.61 17.38
16.78 19.65 21.05 15.35 18.39 20.91 14.15
8.64 8.59 8.94 8.93 6.97 8.58 7.53
Note: CFDirect means direct carbon footprint; CFClothes means clothing production and cleaning carbon footprint; CFFood means food production and processing carbon footprint; CFHouse means house construction carbon footprint; CFTravel means travel carbon footprint.
Table 4 Five components of carbon footprint (t co2) and Gini coefficient of seven household management modes. Management mode
CFDirect
CFClothes
CFFood
CFHouse
CFTravel
CFTotal
Small livestock breeding-oriented Big livestock breeding-oriented Mixed small/big livestock breeding Grassland byproducts-oriented Pasture leasing-oriented Non- livestock -oriented Mixed with breeding and non- livestock Gini coefficient
3.72 3.49 2.93 2.58 2.66 2.99 4.35 0.38
0.04 0.04 0.04 0.03 0.03 0.05 0.04 0.32
1.43 1.42 1.41 1.23 1.26 1.63 1.25 0.29
1.17 1.36 1.37 0.78 0.97 1.39 1.02 0.31
0.6 0.59 0.78 0.45 0.37 0.57 0.54 0.37
6.96 6.90 6.53 5.07 5.29 6.63 7.22 0.26
Note: CFDirect means direct carbon footprint; CFClothes means clothing production and cleaning carbon footprint; CFFood means food production and processing carbon footprint; CFHouse means house construction carbon footprint; CFTravel means travel carbon footprint. CFTotal means total household carbon footprint.
Fig. 5. Differences of carbon efficiency among seven household management modes. If the letters marked for two management modes are completely different, there are significant differences between these two management modes (P < 0.05). Seven household management modes were labeled as I, II, III, Ⅳ, Ⅴ, Ⅵ and Ⅶ, representing small livestock breeding-oriented (I), big livestock breeding-oriented (II), mixed small/ big livestock breeding (III), grassland byproduct-oriented (Ⅳ), pasture leasing-oriented (Ⅴ), non-livestock -oriented (Ⅵ), and mixed with breeding and non-livestock (Ⅶ), respectively.
as food, fuels and the house itself; when household income is higher, the household carbon footprint is also used to buy a car and for travelling etc., to improve the quality of life (Majid et al., 2014; Sommer and Kratena, 2017). Our study also supports this viewpoint. As the per capita disposable income of Inner Mongolia is lower than the national average (National Bureas of Statistics), household consumption is mainly concentrated on basic necessities such as fuel, food and housing. This leads to the direct carbon footprint, food production and processing carbon footprint, and
housing construction carbon footprint being ranked in the top three (Table 4). Meanwhile, the Gini coefficient shows that the greatest inequality among the seven management modes was in the direct household carbon footprint (Table 4). The main reason for this may be differences in energy utilization. In the pastoral areas of Inner Mongolia, there are three main energy sources: raw coal, electricity, and cow dung (Zhu et al., 2012). Because herdsmen households are often distributed very sparsely, the power grid can only cover a limited area. The herdsmen households in these limited areas mainly use electricity for energy, while those of other areas have to use raw coal or cow dung. The differences in CO2 emissions among raw coal, electricity and cow dung lead to the greatest inequality in the direct carbon footprint among the seven management modes. Despite the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode generating moderate levels of income (Fig. 2), the carbon efficiencies of these two modes rank in the top two (Fig. 4). Therefore, these are considered to be the two most optimal management modes for this typical steppe region of Inner Mongolia. Many studies have confirmed that resource allocation and utilization efficiency are key factors in determining carbon efficiency (Eldesouky et al., 2018; Neto et al., 2018; Pan et al., 2016). The highest carbon efficiency of the grassland byproduct-oriented management mode may be due to that mode having the lowest carbon emissions (Fig. 4). This kind of management mainly involves the production and sale of hay, with almost no livestock. Thus the very low consumption for livestock production leads to a significant reduction of the household carbon footprint, and the highest carbon efficiency. For the mixed small/large livestock breeding management mode, since large livestock (cattle, horses) and small livestock (sheep) have different feeding preferences (Cuchillo-Hilario et al., 2018), the mixed small/large livestock breeding can make full use of the grassland resources, and further result in higher carbon efficiency (Wang et al., 2019). Zhu et al. (2018) recommended the strengthening of land use rights transfers, to mitigate negative impacts on the environment. However, our study found that the pasture leasing-oriented management mode had the lowest carbon
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efficiency among the seven management modes (Fig. 5). This may be connected with this management mode having the lowest economic efficiency (Fig. 3). Many studies have found that land leasing can improve overall economic efficiency, especially the economic efficiency of the renter, which will generally increase significantly; though the economic efficiency of the leaser often shows no significant change, or even a decrease (Chamberlin and Ricker-Gilbert, 2016; Pender and Fafchamps, 2006). Therefore, the adoption of the pasture leasing-oriented management mode should be generally cautious. Although the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode are considered to be the two optimal management modes in the typical steppe region of Inner Mongolia, their adoption is restricted by a series of factors, including educational level, labor force and others (Table 2). Therefore, in order to reduce carbon emissions and develop low-carbon livestock, local government intervention will be crucial. Firstly, our study found that small livestock (mainly sheep) breeding-oriented was the dominant management mode in this region (Table 2), so it is reasonable to improve the proportion of large livestock (mainly cattle). Although Inner Mongolia proposed a policy of “reducing sheep and increasing cattle” in 2016, its effect has not been good because of the limited traditional consciousness of the herders. The local government should therefore step up its efforts to promote the policy. Secondly, the herders’ traditional energy source is cattle and sheep manure, and the carbon efficiency of this method is very low. Considering the largest proportion of the direct carbon footprint (Table 3), it is reasonable to increase the use of electricity in this region. Many studies have found that technology has a large role in reducing carbon emissions, and is willingly adopted (Pan et al., 2016; Zhou et al., 2018). Increasing the power grid coverage as much as possible, and also promoting the use of wind and solar hybrid generators in remote pastoral households, will be very useful for reducing carbon emissions. In addition, traditional livestock feeding methods do not use any forage additives in this region, and some forage additives are very effective in reducing methane emissions (Luo et al., 2015). Therefore, it is possible to consider introducing forage additives to reduce carbon emissions. Finally, the method of selling livestock products in this region is relatively simple, with most being sold to small businessmen. The government should therefore cooperate with the herdsmen to build a number of distinctive livestock product brands around beef and lamb, and thus further expand their markets. This can achieve the goal of reducing the livestock number and carbon emissions without reducing the income of the herdsmen. 5. Conclusion It is of great importance to explore the carbon footprint of different livestock management modes, in order to reduce greenhouse gas emissions and mitigate global warming. Inner Mongolia has the most livestock of any province in China. Based on a survey of 404 herdsmen households in a typical steppe region of Inner Mongolia, we found that there are seven different management modes, and that the region is dominated by the small livestock breeding-oriented management mode. There were significant differences of household income, household carbon footprint and carbon efficiency among the seven management modes. In view of the lower carbon footprint and higher carbon efficiency, the grassland byproduct-oriented management mode and the mixed small/large livestock breeding management mode are considered to be the two most optimal management modes in this region. Since it has the lowest household income and the lowest carbon efficiency, the adoption of the pasture leasing-oriented management mode should be cautious.
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