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Journal of Resources and Ecology >

2019 , Vol. 10 >Issue 6: 621 - 631

DOI: https://doi.org/10.5814/j.issn.1674-764X.2019.06.007

Ecosystem Services

Research on the Patterns and Evolution of Ecosystem Service Consumption in the “Belt and Road”

  • ZHANG Changshun , 1, 2, * ,
  • ZHEN Lin 1, 2 ,
  • LIU Chunlan 3 ,
  • LIANG Yihang 1, 4
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  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Beijing Municipal Research Institute of Environmental Protection, Beijing 100037, China
  • 4. School of Earth Science and Resource, Chang’an University, Xi’an 710000, China;
ZHANG Changshun, E-mail:

Received date: 2019-07-01

  Accepted date: 2019-08-09

  Online published: 2019-12-09

Supported by

The Strategic Leading Science and Technology Project of the Chinese Academy of Sciences (Category A)(XDA20010202)

The National Key Research & Development Program of China(2016YFC0503403)

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Copyright reserved © 2019
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Abstract

With great significance in ecosystem protection and sustainable development, the study of ecosystem service consumption (ESC) has become a hot topic in ecological research. Based on FAOSTAT data, in this study the patterns, composition and evolution of ESC and ecosystem service consumption patterns (ESCP) in the “Belt and Road” were revealed on the total and regional scales, taking consumed-biomass as a main indicator. Three main conclusions were reached. 1) The total ESC was mainly contributed from farmland ecosystems along the “Belt and Road” , followed by grassland ecosystems. The ESC indicators on the whole system scale fluctuated, but increased from year 2000 to year 2016. The total ESC increased from 12911.89 Tg yr -1 to 16810.00 Tg yr -1, and the annual per capita consumption of ecosystem services increased from 3.3228 million g p -1 yr -1 to 3.6392 million g p -1 yr -1. 2) The ESC, composition and evolution varied significantly among countries, zones and ecosystems. The annual per capita ESC was highest in Mongolia on the national scale, and highest in Central and Eastern Europe and lowest in Southeast Asia on zone scale, which represented the results from the joint effects of regional resource endowments, consumption habits, levels of productive forces, and other factors. 3) Higher farmland ESC was the dominant ESCP, which accounted for about 76.7% of the total area along the “Belt and Road”, followed by higher farmland + higher grassland ESC, which accounted for about 19.0% of the total area. The other consumption patterns (i.e., those of higher grassland ESC, higher forestland ESC or higher farmland + higher forest + higher grassland ESC) were found in only a few countries. The ESCP may be related to higher regional population density or the higher proportions of developing countries. Therefore, to realize sustainable social, economic and ecological development, and to improve people's well-being, countries along the “Belt and Road” should take advantage of their own resources in developing industries, actively expand trade, achieve mutual benefits and win-win situations, and adjust and optimize consumption patterns of ecosystem services. This study can provide data support for further research on the mechanism of ESCP formation this area.

Key words： ecosystem services; ecosystem service consumption; farmland ecosystem service consumption; grassland ecosystem service consumption; forest ecosystem service consumption; ecosystem service consumption pattern

Cite this article

ZHANG Changshun , ZHEN Lin , LIU Chunlan , LIANG Yihang . Research on the Patterns and Evolution of Ecosystem Service Consumption in the “Belt and Road”[J]. Journal of Resources and Ecology, 2019 , 10(6) : 621 -631 . DOI: 10.5814/j.issn.1674-764X.2019.06.007

1 Introduction

Ecosystem service consumption (ESC) refers to society’s consumption, utilization and occupation of ecosystem goods and services, which is the embodiment of ecosystem service value, and can be measured in physical and monetary terms, respectively, through a combination of the above two methods (Zhen et al., 2008). The ESC significantly affects ecosystem structure and ecosystem services. For example, the exploitation and utilization of forest resources can inevitably lead to variations in forest density, tree species structure, forest age-class distribution and canopy density, and these changes can further influence forest hydrological processes, such as canopy interception, throughfall and the hydrological function of leaf litter. Furthermore, forest soil structure can be destroyed during harvesting and forest regeneration, which could reduce soil erosion resistance of forestland, and ultimately significantly affect ecosystem services such as forest water conservation, soil conservation and carbon sequestration in logging sites. Global population growth and economic development were the main causes of the increase in ESC, which has increased the pressure on ecosystems (Zhen et al., 2012), thereby profoundly changing the patterns and processes of terrestrial ecosystems, and reducing the capacities of ecological service supply and sustainable development. The contradiction between maintaining ecosystem service function and economic growth is becoming more and more prominent (Vitousek et al., 1997; Liu, 2005). Studies have shown that the intensity of human utilization of ecosystems today is 2.5 times greater than that of 50 years ago, which had already exceeded 20% of carrying capacity (World Wildlife Fund, 2004). As a result, 60% of the world’s ecosystems have been degraded (Millennium Ecosystem Assessment, 2005), and the human demand for and consumption of ecosystem goods and services from degraded ecosystems are still increasing, which poses a serious threat to the needs of our descendants (Norbaard, 2009). In ecosystems, the energy decreases greatly as it flows along the food chain from green plants, and eventually only a small amount of energy is retained for growth and forming animal tissue. This principle is known as “the Ten Percent Law” (Lindeman, 1942), and means that the ESC of eating 1 kg of meat is much higher than that of eating 1 kg of vegetables or fruits. Thus, there are significant differences in the occupation of an ecosystem among different consumption patterns of ecosystem services (Yan et al., 2012). This is the motivation for humanity to change existing consumption patterns of ecosystem services after scientists deduced the possible impacts of current human ESC on future generations, potentially resulting in a complete lack of necessary ecosystem services being available to future generations for their consumption needs (Norbaard, 2009). However, constructing a scientific and reasonable model to match supply and consumption of ecosystem services has become an important scientific issue in trying to establish a resource-saving society and sustainable development. Therefore, studying the mechanisms of ESC will become a new field of ecosystem research.
Research on the consumption mechanism of ecosystem services is still in the starting stage. After considering the interactions between ecosystem services, consumption and management, Zhen et al. (2008) defined the concept of ESC, categorized it into direct consumption and indirect consumption, proposed a method for distinguishing direct vs. indirect products of ecosystem services, and finally addressed the expression of ESC. Based on existing case study results and first hand household data, that team analyzed the characteristics, paths, quantity, and mechanisms of ESC, expounded the impacts of variations in ESC and their patterns on ecosystems, discussed the transformation of household level analysis to regional level analysis, and considered an Agent Based Modeling (ABM) approach which was very useful for establishing the most reasonable ESCP at the regional level (Zhen et al., 2012). Subsequently, they built the multi-agent modeling method for reasonable consumption of ecosystem services, based on a case study of the Farming Pastoral Zone in Inner Mongolia (Pan et al., 2012), and established the measurement method of reasonable consumption for ecosystem productivity supply services (Yan et al., 2012). The above-mentioned research generated positive discussions and practices on the connotation, research theory and methods of ESC study. In view of the fact that ecosystem biomass consumption reflects the impact of human use intensity on the supply of ecosystem services, ESCP and regional sustainability indicators that reflect their spatial patterns (Haberl, 1997; Imhoff et al., 2004; Helmut et al., 2007), this study explored the evolution of ESCP using biomass consumption along the “Belt and Road” .
The countries along the “Belt and Road”, including China, South Asia, Southeast Asia, West Asia, Central Asia, Russian and Mongolia Area (Russia, Belarus, Belarus, Ukraine, Moldova, Georgia, Armenia, Azerbaijan and Mongolia), and Central and Eastern Europe account for about 38% of the world's land area, and about 62% of the world total population, but only about 31% of the world’s GDP. Therefore, the “Belt and Road” is mainly composed of middle- income countries, and only 18 of them are considered high- income countries (Wu, 2017). Although research on ESC based on biomass consumption had been carried out for Africa, European Union, China, Philippines, and other regions (Kastner, 2009; Chen et al., 2015; Kastner et al., 2015; Plutzar et al., 2015; Fetzel et al., 2016), there are still no reports on ESC for the “Belt and Road” based on biomass consumption. Therefore, based on yearly FAO statistics for 2000-2016, and after developing a calculation method with biomass consumption as the main indicator, the patterns, composition and evolution of ESC in the “Belt and Road” were studied dynamically on the global and zone scales. The results will contribute to further study on the formation mechanisms of ESCP and sustainable ESC in the “Belt and Road” .

2 Methods and data sources

2.1 Research methodology

(1) Plant-based food consumption
Plant-based food consumption mainly includes the consumption of grains, potatoes, beans, sugar crops, vegetables, fruits and featured plants. The formula for its calculation is:
$C{{B}_{v}}=\mathop{\sum }^{}\frac{(1-M{{C}_{i}})\times CO{{M}_{i}}}{H{{I}_{i}}\times(1-WA{{S}_{i}})}$
where CBv is plant-based food consumption, g; i is the type of plant-based food, such as grains, potatoes, beans, vegetables, etc.; COMi is the consumption of plant-based foods, where consumption = production + import - export, g; MCi is the moisture content of plant-based foods; HIi is the harvest index of i-type plant-based foods; and WASi is the loss coefficient of each type of plant-based food, assigned here as 0.1. The assignments for MCi and HIi are shown in Table 1.
(2) Agricultural product consumption
Agricultural product consumption is the consumption of plant oils, except for woody oil products, and sugar products. The formula for its calculation is:
$\begin{align} & C{{B}_{o}}=\mathop{\sum }^{}\frac{(1-M{{C}_{pi}})\times CO{{M}_{pi}}/{{\mu }_{pi}}}{H{{I}_{pi}}\times(1-WA{{S}_{pi}})}+ \\ & \ \ \ \ \frac{(1-M{{C}_{b}})\times CO{{M}_{b}}/{{\mu }_{b}}}{H{{I}_{b}}\times (1-WA{{S}_{b}})} \\ \end{align}$
where CBo is the biomass of agricultural products consumption, g; COMpi and COMb are the consumptions of each plant oil (e.g. peanut oil, sesame oil, soybean oil, olive oil, etc.) and sugar products, respectively, g; μpi and μb are oil yield and sugar yield, assigned here as 0.35 (Xiao, 2009); MCpi and MCb are the water contents of oil crops and sugar beets, respectively; HIpi and HIb are the harvest indices of each oil crop and sugar beets, respectively; and WASpi and WASb are the loss coefficients of oilcrops and sugar beets , respectively, all assigned here as 0.1.
Table 1 The harvest index and moisture content of different crops
Crop type Harvest index Moisture content Crop type Harvest index Moisture content
Rice 0.50 0.13 Rapeseed 0.26 0.09
Maize 0.49 0.13 Sesame 0.34 0.09
Wheat 0.46 0.13 Sunflower 0.32 0.09
Grain 0.31 0.13 Other
oilseeds		 0.36 0.09
Other Cereals 0.38 0.13 Sugar beets 0.71 0.133
Sorghum 0.31 0.13 Sugarcane 0.7 0.133
Barley 0.49 0.13 Fiber Crops 0.38 0.133
Beans 0.42 0.13 Tobacco 0.61 0.082
Potato 0.59 0.133 Vegetables 0.49 0.82
Other roots 0.67 0.133 Fruits 0.49 0.82
Cotton 0.16 0.083 Tea 0.71 0.08
Peanut 0.50 0.09 - - -
(3) Animal food consumption
Animal food consumption refers to the biomass consumption required to produce the animal products. The formula for its calculation is:
$C{{B}_{m}}=\mathop{\sum }^{}\frac{CO{{M}_{i}}\times {{\delta }_{i}}\times(1-MC)}{HI\times (1-WAS)}$
where CBm is the biomass consumed for animal foods, g; COMi is the actual quantity of meat, eggs and milk consumed, g; δi is the grain discount coefficient of animal foods, here values of 7, 3 and 0.5 were used for meat, eggs and milk, respectively (Guo et al., 2013); MC is the moisture content of grain (0.133); HI is the harvest index of the grain crop factor, assigned here as 0.49; and WAS is the loss coefficient (0.1).
(4) Live animal consumption
Live animal consumption refers to the consumption needed to feed live animals. The formula for its calculation is:
$C{{B}_{g}}=\mathop{\sum }^{}{{N}_{i}}\times {{\gamma }_{i}}\times GW\times G{{D}_{i}}\times (1-MC)\times 1000$
where CBg is the biomass consumption of live animals, g; Ni is the number of live animals, head/no.; γi is the standard sheep conversion coefficient for different types of live animals, and its assignments are shown in Table 2; GW is the hay weight needed for a standard sheep each day, assigned here as 1.8 kg/d; GDi is the days of feeding, here 365 days for livestock animals, and 180 days for slaughter animals; and MC indicates the moisture content of dried grass, calculated as 14% (Du, 2018).
(5) Wood forest product consumption
Wood forest product consumption mainly refers to roundwood and wood pulp consumption. The formula for its calculation is:
$C{{B}_{f}}=F\left( VO{{L}_{i}}\times L{{R}_{i}} \right)$
where CBf is the biomass consumption for wood forest products, g; VOLi is the consumption for various wood forest products, m3; LR is the equivalence coefficient of wood forest products converted into roundwood, as detailed in the literature (Tian et al., 2016); and F(x) is the conversion function for biomass and accumulation of different forest stand types.
Table 2 The conversion coefficients of various livestock into standard sheep units
Livestock Conversion coefficient Livestock Conversion coefficient
Sheep 1.0 Mule 5.0
Goat 0.8 Camel 8.5
Cattle 6.5 Rabbit 0.125
Buffalo 7.0 Pig 1.5
Yak 4.5 Goose 0.2
Horse 5.5 Chicken 0.05
Ass 3.0 Pigeon 0.02
(6) Ecosystem service consumption (ESC)
ESC equals the total consumption of plant-based foods, agricultural products, animal foods, live animals, and wood forest products.
(7) Classification method
Forest ecosystem service consumption (FESC) equals the total consumption of wood forest products, woody fruits (e.g., apples, pears, peaches, apricots, cherries and nectarines), woody plant oils (olive oil, tea oil, palm oil, etc.), nut consumption and tea consumption. Grassland ecosystem service consumption (GESC) equals the consumption of animal foods and live animals, except for pork or pig consumption and poultry products.
Mathematically, farmland ecosystem service consumption (AESC) = ecosystem service consumption - FESC - GESC, which equals plant- based food consumption + Agricultural product consumption - Non-timber forest products (e.g., apples, pears, oranges, hazelnuts, pine nuts, pistachio nuts and woody plant oils) + Pork/pig and poultry products.

2.2 Data sources

Agricultural, forestry and animal husbandry production data, and import and export data, from years 2000 to 2016 were obtained from the UN Food and Agriculture Organization database (http://www.fao.org/faostat/en/#data). Population data from years 1990-2016 were obtained from fast-access data (https://www.kuaiyilicai.com/stats). National boundary data of the “Belt and Road” route were from the national development and reform commission. The remaining computational parameters were all derived from the published literature.

3 Research results

3.1 Pattern and evolution of global ESC

There were significant differences in ESC values and their compositions among the countries along the “Belt and Road” in 2016. The annual per capita ESC was the highest in Mongolia, about 21.393 million g p-1 yr-1, followed by Estonia and Bhutan, about 11.903 million g p-1 yr-1 and 10.834 million g p-1 yr-1, respectively. They were followed by Russia, Kazakhstan, Turkmenistan, Belarus, Lithuania, Poland, Bosnia and Herzegovina, Serbia, other Central and Eastern European countries, Oman in Western Asia, as well as Myanmar, Laos and Malaysia in Southeast Asia, which had annual per capita ESC ranging between 5 and 10 million g p-1 yr-1. The last group included India, Bangladesh and Afghanistan in South Asia and Syria, Iraq, Jordan and Yemen in West Asia that were lower still, less than 3 million g p-1 yr-1 (Fig. 1).
Fig. 1 The per capita ESCP of countries along the “Belt and Road” in 2016
Because of the remarkable differences in topography and land use composition, the composition of annual per capita ESC was significantly different between the countries along the “Belt and Road”. The countries with higher annual per capita GESC were mainly distributed in Central Asia and Mongolia, while those with lower values were mainly in Southeast Asia. The countries with higher annual per capita AESC were mainly located in Southeast Asia, Central and Eastern Europe, and included Belarus and Oman in West Asia. The countries with higher annual per capita FESC were mainly distributed in the northern areas, such as Russian Federation, Belarus, Poland, Czech Republic, Slovakia, Croatia, Montenegro, Serbia, Bulgaria and other countries in Central and Eastern Europe, as well as in the areas with rich forest resources in Southeast Asia, such as Myanmar and Laos (Fig. 1).
The total and annual per capita ESC significantly increased at the scale of the whole the “Belt and Road” , and the total ESC increased from 12911.89 Tg in 2000 to 16810.00 Tg in 2016, representing a net increase of about 30%. The annual per capita ESC also increased, from 3.3228 million g p-1 yr-1 in 2000 to 3.6392 million g p-1 yr-1 in year 2016, representing a net increase of about 9.5% (Fig. 2).
Fig. 2 Evolutions of ESC and its composition from year 2000 to 2016
The largest contributor to total ESC in the “Belt and Road” was farmland, which contributed about 61% of the total ESC, followed by grassland at 26% of the total ESC, and forestland was the lowest, about 13% of the total ESC. The increase in the annual per capita ESC of the whole region of the “Belt and Road” was mainly due to the increase of annual per capita AESC, followed by FESC, while the annual per capita GESC showed a trend of reduction (Fig. 2).

3.2 Pattern and evolution of regional ESC

The total annual ESC for the “Belt and Road” was mainly from China, South Asia and Southeast Asia, and their annual ESC values were 4881.74 Tg yr-1, 4339.0 Tg yr-1 and 2413.7 Tg yr-1, respectively, or about 32.7%, 29.1% and 16.2% of the total annual ESC, and their sum was about 78.0% of the total (Fig. 3). They were followed by the Western Asian and Russian and Mongolia Area, at about 8.1% and 7.6% of the total annual ESC, respectively; and the lowest were countries in Central Asia (about 291.4 Tg yr-1), about 2.0% of the total annual ESC (Fig. 3).
Fig. 3 Evolution of ESC values during 2000-2016
The highest average annual per capita ESC was in Central and Eastern Europe, about 5.525 million g p-1 yr-1, followed by Russian and Mongolia Area, about 5.097 million g p-1 yr-1, the third was Central Asia, about 4.705 g p-1 yr-1, and the lowest was South Asia, about 2.741 million g p-1 yr-1. With the exception of Central and Eastern Europe, the total and annual per capita ESC showed increasing volatility, and the highest increase in total ESC was in China, which increased from 4265.9 Tg in 2000 to 5527.5 Tg in 2016, a net growth of about 30%. Meanwhile, the highest increase in annual per capita ESC was in Central Asia, which increased from 3.67 million g p-1 yr-1 in 2000 to 5.098 million g p-1 yr-1 in 2016, a net growth of about 38.9% (Fig. 3).
The GESC in the “Belt and Road” was mainly contributed from Southeast Asia and China, with averages of 1835.7 Tg yr-1 and 846.5 Tg yr-1, respectively, or about 47.5% and 21.9% of the total GESC, and their sum was about 69.4% of the total. The second largest contributions were from West Asia, Southeast Asia, and Russian and Mongolia Area , with about 8.8%, 8.0% and 7.5% of the total, respectively, and the lowest was Central and Eastern Europe (100.3 Tg yr-1), or about 2.6% of the total (Fig. 4).
Fig. 4 Evolution of GESC values during 2000-2016
The highest annual per capita GESC was in Central Asia, about 2.319 million g p-1 yr-1, followed by the Russian and Mongolia Area,, with an average of 1.307 million g p-1 yr-1, and the lowest was in Southeast Asia, about 0.529 million g p-1 yr-1. Except for Central and Eastern Europe, and Russian and Mongolia Area, the total GESC of the others showed trends of increasing volatility. However, the annual per capita GESC showed a fluctuating decreasing trend, except for Central Asia and Southeast Asia (Fig. 4).
The AESC of the “Belt and Road” was mainly contributed from China, South Asia and Southeast Asia, with averages of 3584.7 Tg yr-1, 1873.2 Tg yr-1 and 1731.2 Tg yr-1, respectively, or about 39.4%, 20.5% and 19.0% of the total AESC, with their sum representing about 78.8% of the total. They were followed by West Asia, Russian and Mongolia Area, with about 8.2% and 6.8% of the total, respectively, and the lowest was Central Asia (140.5 Tg yr-1), or only about 1.5% of the total (Fig. 5).
Fig. 5 Evolution of AESC values during 2000-2016
The highest annual per capita AESC was in Central and Eastern Europe, at about 3.053 million g p-1 yr-1, followed by Southeast Asia and Central Asia, at about 2.933 million g p-1 yr-1 and 2.791 million g p-1 yr-1, respectively, and the lowest was in South Asia, at only about 1.180 million g p-1 yr-1. Except for Central and Eastern Europe, where the total and annual per capita AESCs were fluctuating and decreasing, the others were fluctuating and increasing, with the increase of AESC being highest in China, and the highest increase of annual per capita AESC was in Southeast Asia (Fig. 5).
The FESC of the “Belt and Road” was mainly contributed from South Asia, China and Southeast Asia, at about 630.1 Tg yr-1, 450.5 Tg yr-1 and 373.4 Tg yr-1, respectively, or about 32.3%, 23.1% and 19.1% of the total FESC. The second largest contributions were mainly from Russian and Mongolia Area, Central and Eastern Europe and Western Asia, at about 11.4%, 7.5% and 6.2% of the total, respectively, and the lowest was Central Asia (6.8 Tg yr-1), with only about 0.3% of the total (Fig. 6).
The highest annual per capita FESC was in Central and Eastern Europe, where the average was 1.203 million g p-1 yr-1; followed by Russian and Mongolia Area and Southeast Asia, at about 0.999 million g p-1 yr-1 and 0.645 million g p-1 yr-1 respectively; and the lowest was Central Asia, at about 0.108 million g p-1 yr-1. The annual per capita FESC in South Asia, China and Southeast Asia were fluctuating and decreasing, while it was fluctuating and increasing in the other areas, and it increased the most in Central and Eastern Europe (Fig. 6).
Fig. 6 Evolution of FESC values during 2000-2016

3.3 Evolution of ESCP

According to the results of cluster analysis, the consumption patterns of ecosystem services along the “Belt and Road” were divided into five categories: higher farmland (I), higher forestland (II), higher grassland (III), higher farmland + higher grassland (IV), and higher farmland + higher forestland + higher grassland (V). Pattern I refers to cases where the proportion of AESC in annual per capita ESC was much larger than proportions of GESC or FESC, about 63.3%. Pattern II refers to cases where the proportion of FESC in annual per capita ESC was much higher than proportions of AESC or GESC. Pattern III refers to cases where the proportion of GESC in annual per capita ESC was much higher than proportions of AESC or FESC. Pattern IV refers to cases where there was no difference between the proportions of AESC and GESC in annual per capita ESC, and their proportions were far higher than that of FESC. While the V pattern refers to cases where AESC, FESC and GESC were equally important and comparable, their proportions in annual per capita ESC were each more than 20% (Table 3).
Table 3 The consumption patterns of ecosystem services in the “Belt and Road” countries
Consumption patterns Average ratios of grassland/farmland/
forestland (%)		 2000 2005 2010 2016
Number of counries Area
(104 km2)		 Number of countries Area
(104 km2)		 Number of countries Area
(104 km2)		 Number of countries Area
(104 km2)
Higher farmland (I) 20.54/63.34/16.11 37 3641.92 45 4068.22 44 3888.74 42 3919.80
Higher forestland (II) 14.63/21.07/64.29 3 14.75 1 3.76 3 14.75 5 18.20
Higher grassland (III) 93.68/3.85/2.47 1 156.58 1 156.58 1 156.58 1 156.58
Higher farmland + higher grassland (IV) 48.72/44.30/6.98 20 1131.26 16 818.05 16 964.58 16 930.07
Higher farmland + higher forest + higher grassland (V) 21.52/44.69/33.79 4 113.09 2 11.00 1 32.95 1 32.95
Pattern I was the most important ESCP in the “Belt and Road” , and there were about 42 countries under this consumption pattern, which represented an area of 38.7967 million km2, or about 76.7% of the total area of the “Belt and Road” . Pattern III was the second most prevalent ESCP pattern, and there were 17 countries under this consumption pattern, with a combined area of 9.6099 million km2, accounting for 19.0% of the total area. There were only a few countries under each of the II, IV and V consumption patterns (Table 3).
Countries under pattern IV were mainly located in Central Asia and South Asia, while countries under pattern I were widely distributed outside Central Asia and South Asia. Mongolia was the only country where GESC was the dominant ESC. Countries under pattern II were mainly distributed in Central and Eastern Europe, such as Estonia, Latvia, Montenegro and Slovenia, and included Bhutan in Southeast Asia. Countries under pattern V were mainly distributed in Southeast Asia, such as Myanmar, Laos, Cambodia, Malaysia, and others. The ESCP of countries along the “Belt and Road” at different times are detailed in Fig. 7, and this evolution is the result of the comprehensive effects of national land use composition, international trade and food culture, and many others.
Fig. 7 Evolution of consumption patterns along the “Belt and Road”

4 Conclusions

This study reached four main conclusions.
(1) There were remarkable differences in ESC values and their compositions among countries along the “Belt and
Road”. The highest annual per capita ESC was in Mongolia in year 2016, about 213.93 million g p-1 yr-1, and the lowest was in Palestine, at only about 0.9741 million g p-1 yr-1. The indices of ESC for the whole area of the “Belt and Road” were fluctuating and increasing. The total ESC increased from 12911.89 Tg in year 2000 to 16810.00 Tg in year 2016, and the annual per capita ESC increased from 3.33 million g p-1 yr-1 in 2000 to 3.64 million g p-1 yr-1 in 2016. The overall ESC in this region was mainly due to AESC, at about 61% of the total ESC, followed by GESC, at about 26% of the total ESC, and FESC was the least important, at only 13% of the total ESC. The increase of annual per capita ESC at the scale of the whole region was mainly attributed to the increase of AESC, which was the result of the resource endowments, food habits and development and application of modern agricultural technology.
(2) AESC, GESC and FESC, were all mainly from China, South Asia and Southeast Asia at the scale of regional zones. Although the lowest total GESC was in Eastern and Central Europe, the lowest totals of AESC and FESC were each in Central Asia, as was the lowest total ESC, which was the result of regional resource endowments and population distributions. For annual per capita ESC, except for the highest GESC in Central Asia, the others were all highest in Central and Eastern Europe, which was the result of the joint action of regional socio-economic development levels and ESCP.
(3) The GESC was fluctuating and decreasing in Russian and Mongolia Area, and Central and Eastern Europe at the regional zone scale, and the others along the “Belt and Road” were all fluctuating and increasing. The evolutionary trend of annual per capita ESC varied among different zones and different ecosystems, while the annual per capita ESC and AESC of all zones were fluctuating and increasing from year 2000 to year 2016.
(4) Pattern I was the main ESCP, which was widely distributed along the “Belt and Road” except for Central Asia and South Asia, and was found in countries that together accounted for about 76.71% of the total area of the “Belt and Road” . Pattern IV was the second most widespread ESCP, and the countries under this pattern were mainly distributed in Central Asia and South Asia, and accounted for 19.0% of the total area. Only a few countries were under the other patterns of ESC, which was consistent with the fact that countries along the “Belt and Road” were mainly developing countries.
This study can provide data support for further research on the formation mechanisms of ESCP along the “Belt and Road”.

References

Publishing order | Descend order by publishing year | Descend order by cited within
1
Chen A, Li R, Wang H , et al. 2015. Quantitative assessment of human appropriation of aboveground net primary production in China. Ecological Modelling, 312(24):54-60.

DOI

2
Du W . 2018. Study on Ecological Carrying Capacity based on Ecological Supply and Consumption—Taking Hainan for Example. MSc diss., Chang' an University. (in Chinese).

3
Fetzel T, Niedertscheider M, Haberl H , et al. 2016. Patterns and changes of land use and land-use efficiency in Africa 1980-2005: An analysis based on the human appropriation of net primary production framework. Regional Environmental Change, 16(5):1507-1520.

DOI

4
WWF (World Wildlife Fund). 2004. Living Planet Report. Gland, Switzerland: Avenue du Mont-Blanc.

DOI PMID

5
Guo H, Cai J, Yang Z . 2013. Modeling for measuring city food footprint with applied empirical analysis. Journal of Natural Resources, 28(3):417-425. (in Chinese)

DOI

6
Haberl H . 1997. Human appropriation of net primary production as an environmental indicator: implications for sustainable development. Ambio, 26(3):143-146.

7
Helmut H, K Heinz E, K Fridolin , et al. 2007. Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 104(31):12942-12947.

DOI PMID

8
Imhoff M L, Bounoua L, Ricketts T , et al. 2004. Global patterns in human consumption of net primary production. Nature, 429(6994):870-873.

DOI PMID

9
Kastner T . 2009. Trajectories in human domination of ecosystems: Human appropriation of net primary production in the Philippines during the 20th century. Ecological Economics, 69(2):260-269.

DOI

10
Kastner T, Erb K, Haberl H . 2015. Global Human Appropriation of Net Primary Production for Biomass Consumption in the European Union, 1986- 2007. Journal of Industrial Ecology, 19(5):825-836.

DOI PMID

11
Lindeman R L . 1942. The trophic-dynamic aspect of ecology. Ecology, 23(4):399-418.

DOI

12
Liu J Y. 2005. Integrated Ecosystem Assessment of Western China. Beijing: China Meteorological Press.

13
Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being: Synthesis. Washington D C: Island Press.

14
Norbaard R B . 2009. Ecosystem services: From eye-opening metaphor to complexity blinder. Ecological Economics, 69(6):1219-1227.

DOI

15
Pan L, Yan H, Huang H , et al. 2012. Multi-agent modeling method of reasonable consumption of ecosystem service:A case of the Farming Pastoral Zone in Inner Mongolia. Resources Science, 34(6):1007-1016. (in Chinese)

16
Plutzar C, Kroisleitner C, Haberl H , et al. 2015. Changes in the spatial patterns of human appropriation of net primary production (HANPP) in Europe 1990-2006. Regional Environmental Change, 41(7):1-14.

17
Tian M, Shi Y, Huang Y , et al. 2016. An empirical analysis of effects of economic development and forest product trade on wood consumption in china. Scientia Silvae Sinicae, 52(9):113-123. (in Chinese)

DOI

18
Vitousek P M, Mooney H A, Lubchenco J , et al. 1997. Human domination of Earth’s ecosystems. Science, 277(5325):494-499.

DOI

19
Wu S . 2017. Economic development of countries along the Belt and Road. Review of Economic Research, 15:16-45. (in Chinese)

DOI PMID

20
Xiao J . 2009. The dependence of traditional brewing industry and grain production in the republic of China (1912-1949). Social Science Journal, ( 2):139-145. (in Chinese)

21
Yan H, Zhen L, Li F , et al. 2012. Measurement method of reasonable consumption for ecosystem productivity supply service: A case study of Inner Mongolia grassland transect. Resources Science, 34(6):998-1006. (in Chinese)

22
Zhen L, Liu X, Wei Y . 2008. Consumption of ecosystem services: models,measurement and management framework. Resources Science, 30(1):100-106. (in Chinese)

23
Zhen L, H Yan, Y Hu , et al. 2012. Consumption of ecosystem goods and services and its impact assessment. Resources Science, 34(6):989-997. (in Chinese)

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