Emergy-based sustainability assessment of forest ecosystem with the aid of mountain eco-hydrological model in Huanjiang County, China

Journal Pre-proof Emergy-based sustainability assessment of forest ecosystem with the aid of mountain eco-hydrological model in Huanjiang County, China

Chesheng Zhan, Ruxin Zhao, Shi Hu PII:

S0959-6526(19)34508-1

DOI:

https://doi.org/10.1016/j.jclepro.2019.119638

Reference:

JCLP 119638

To appear in:

Journal of Cleaner Production

Received Date:

13 May 2019

Accepted Date:

08 December 2019

Please cite this article as: Chesheng Zhan, Ruxin Zhao, Shi Hu, Emergy-based sustainability assessment of forest ecosystem with the aid of mountain eco-hydrological model in Huanjiang County, China, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.119638

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Journal Pre-proof The first author Given name: Chesheng Family name: Zhan Affiliations: 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2. Yucheng Comprehensive Experiment Station, Chinese Academy of Science, Beijing 100101, China E-mail: [email protected] The second author Given name: Ruxin Family name: Zhao Affiliations: College of water sciences, Beijing Normal University, Beijing, 100875, China E-mail: [email protected] The third author Given name: Shi Family name: Hu Affiliations: Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China E-mail: [email protected] Mailing address: 11A, Datun Road, Chaoyang District, Beijing, 100101, China Tel.: +86 10 64889671 Fax: +86 10 64851844 Shi Hu is the Corresponding author

Journal Pre-proof Emergy-based sustainability assessment of forest ecosystem with the aid of mountain eco-hydrological model in Huanjiang County, China Abstract: Comprehensive recognition and evaluation of ecosystem development are fundamental for sustainable decision-making and nature conservation. Using Huanjiang Maonan Autonomous (also named Huanjiang) County-a typical karst mountainous region as example, by coupling the emergy analysis and the mountain eco-hydrological model (MECH), the most suitable vegetation coverages for forest ecosystem sustainable development are obtained in different economic development levels. The results showed that the emergy of renewable resources (R), nonrenewable resources (N) and economic feedback input (EFI) accounted for about 69.8%, 12.7%, and 17.5% of the total emergy (U) respectively from 2000 to 2015. Even though the forest ecosystem of Huanjiang maintained sustainable developing tendency; the decrease of the ratio of renewable resources emergy (R) to total emergy (U) resulted in the decrease of emergy selfsupport ratio (ESR), emergy yield ratio (EYR) and emergy sustainability index (ESI), which may threaten the system sustainability. The increase of vegetation coverage reduced soil loss and total runoff, increased economic feedback input. As the results, the renewability (R/U) and emergy investment ratio (EIR) presented an increase trend, while the emergy self-support ratio (ESR), emergy yield ratio (EYR) and environment loading ratio (ELR) showed a tendency of decrease. The system sustainability will reach the optimal state when the vegetation coverage increased by 30%, 20%, and 10% if the GDP value of the county in the future as those in the 10th, 11th, and 12th Five-year Plan in China. Coupling the emergy analysis and mountain eco-hydrological model provides a new approach to understand the harmonious development between forest ecosystem and human beings. Keywords: emergy analysis, mountain eco-hydrological model, vegetation coverage, sustainable development, Huanjiang County

1. Introduction Sustainable development is the foremost human concerns, mainly due to the imbalance between the increase of human demands and the availability of natural resources (George et al.,

Journal Pre-proof 2015; Provasnek et al., 2017). Driven by this imbalance, ecosystem has showed a distinct contradiction among human activity, natural environment and economic development dimensions. Thereby, it is necessary to comprehensively evaluate the performance of different elements in ecosystem. Many methods have been proposed to quantify sustainable development of ecosystem, such as material flow analysis (Barles, 2009; Browne et al., 2011; Calvo et al., 2016), ecological footprint (Huang et al., 2007; Geng et al., 2014), life cycle assessment (Sara et al., 2017) and substance flow analysis (Yuan et al., 2011; Wu et al., 2012). However, these methods neither address the difference of resource flows quantitatively nor take into account the complex interactive relationships between natural environment and economic systems. Compared with the above methods, emergy analysis provides an approach to link the environmental resources and economic capital (Geng et al., 2013), which can explore the complex relationship among ecological system, human society, and economic system (González-Mejia and Ma, 2017). In recent years, emergy analysis has been widely used in the assessments of ecosystem services and regional development. For instance, emergy analysis was used to assess the ecological service function of erhai lake (Zhong et al., 2019), the impact of dam barrier on river ecological environment (Fang et al., 2015), the environmental sustainability of Island (Vega-Azamar et al., 2013; Jung et al., 2018), and the agriculture sustainable development (Ali et al., 2019; Amiri et al., 2019). Additionally, the emergy analysis has been successfully applied for analyzing regional development at province (Yu et al., 2016; Chen et al., 2017), city (Liu et al., 2009a; Wang et al., 2018) and county (Zhang et al., 2018) scale. With the historical data, the emergy analysis can assess the past and current sustainability of the ecosystem, but it is hard to predict the balance between ecosystem and economics under a hypothetical situation. Ecosystem model applies the mathematical approach to quantitatively describe the complex relationships among natural factors in a certain ecosystem. It provides not only a quantitative and comprehensive understanding of the ecosystem, but also the quantitative variation of ecological element, such as: evapotranspiration, gross primary productivity (GPP), which can be used in emergy analysis. Therefore, the emergy analysis method and ecosystem

Journal Pre-proof model may be coupled to predict sustainable development of a certain system (Zhang et al., 2018). Using emergy analysis method and Vegetation Interface Processes (VIP) model, Hu et al (2010) successfully optimized irrigation to achieve the sustainability of an agricultural ecosystem. Karst environment, where exists a sharp contradiction in population-resources-environment (Bai et al., 2013), is facing serious environmental problems, such as soil erosion, rocky desertification (Nie et al., 2019), and productivity decline of agriculture, forestry and livestock husbandry (Chen et al., 2011; Bai et al., 2013). The karst rocky desertification is one of the three major ecological disasters in China, especially in southwest China (Wang et al., 2010a), where nearly 82% of the rocky desertification areas are concentrated (Jiang et al., 2014). The ecohydrological process of the forest ecosystem in karst region is affected by water and human activities (Zhou et al., 2018). The sustainable development of the ecosystem faces the dual pressure of poverty and fragile geo-ecological environment (Wang et al., 2004; Zhou et al., 2019). To alleviate the rocky desertification and improve the ecological environment, since the late 1990s, Chinese government has launched a series of ecological restoration projects (Tong et al., 2017), which has effectively promoted vegetation recovery in the karst regions (Macias-Fauria, 2018). However, the impact of ecological projects on ecosystem processes is complex. On one hand, high investment does not mean high return. The effectiveness of the restoration projects were also affected by other factors, such as climate, topography and human management (Tong et al., 2017). On the other hand, large-scale afforestation may increase vegetation transpiration (Yan et al., 2018) and decrease runoff (Huang et al., 2003) which may intensify the contradiction of water supply and demand in the ecosystem. Using Huanjiang County, which is located in the karst mountainous region in China and undergoing a series of ecological projects, as an example, this paper aims to explore the sustainability of the forest ecosystem in two-fold. First, the emergy analysis method is applied to evaluate the sustainable development of forest ecosystems since 2000. Second, emergy analysis and mountain eco-hydrological model are coupled to determine the optimal vegetation coverage that is most suitable for the sustainable development of the ecosystem.

Journal Pre-proof 2. STUDY AREA AND METHODS 2.1 Study area Huanjiang County (107o51’-108o43’E, 24o44’-25o33’N) is located at middle to east of the karst mountainous region in southwest China (as shown in Figure 1). It covers an area of 4568 km2 and belongs to Hechi city, Guangxi Zhuang Autonomous Region. The elevation is high in the north and low in the south with the highest/lowest elevation of 1639 m/116 m respectively. The study site falls within the central Asian tropical monsoon climate zone. The average annual precipitation is higher in the northern part than southern part, with the amount of 1750 mm and 1389 mm, respectively. Average annual temperature is about 19.9oC, and the average annual runoff is 3.72 billion m3. The soil types of Huanjiang County mainly include yellow loamy sediment (the organic matter content is usually greater than 5%), black calcareous soil (the organic matter content is about 5%-15%), and brown calcareous soil (the organic matter content is about 2%6%). In 2016, the forest accounted for 68.88% of the total area of Huanjiang County. According to the land-use data of 2010 (Figure 1d), Huanjiang County is identified as a forest ecosystem with about 85% of the county covered by forest, shrubs and grasslands.

Insert Figure 1.

2.2 Data sources Six types of data are used in this study, namely: meteorological data, hydrologic data, soil data, the social economic statistical data, the Normalized Difference Vegetation Index (NDVI) and land-use data. The meteorological data including solar radiation and precipitation from 2000 to 2015 are obtained from the National Meteorological Information Center (http://data.cma.cn/). The daily meteorological data in 40 national meteorological stations in and around karst mountainous region from 2000-2015 are obtained from the National Meteorological Information Center (http://data.cma.cn/). The hydrologic data (i.e., runoff) are retrieved from the Annual Hydrological Report P. R. China. Data of soil texture are derived from Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (http://www.resdc.cn/). Social economic statistical data

Journal Pre-proof include the annual cost of infrastructure, sapling, fertilizer, pesticide, farm implement, fuel, electricity; the labor participated in the forest conservation; and the volume of live timber reserves during 2000-2015. These statistical data are obtained from the statistical yearbook of Huanjiang County and Hechi city (2001-2016)，which can be downloaded from China National Knowledge Infrastructure (CNKI) (http://tongji.cnki.net/kns55/brief/result.aspx). With temporal and spatial resolution of 16-day and 250 m, NDVI data from 2000-2015 are derived from MOD13Q1 NASA science data (http://modis.gsfc.nasa.gov/data/datapro /mod13.php). Land-use data of 2010 is obtained from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (http://www.resdc.cn/). Meteorological data (atmospheric pressure, air temperature, relative humidity, wind speed, precipitation, solar radiation), NDVI series and land-use data are used to drive the mountain ecohydrological model (MECH). The meteorological data are interpolated to the whole region, with spatial resolution of 250 m × 250 m, by the method of Gradient Inverse Distance Square (GIDS) (Nalder and Wein, 1998; Lin et al., 2002). In order to construct high-quality NDVI time series, the pixels are excluded in accordance with the following two criteria: (i) if the absolute difference is less than 0.1 between the maximum and minimum of the annual NDVI, since most of these pixels belong to the built-up land; (ii) if the NDVI values are beyond the range of 0-1, since these pixels indicate surface water areas. Finally, the Savitzky-Golay filter are applied to reduce noisy from cloud cover and to solve geometry problems of satellite data (Chen et al., 2004). Land-use data is re-sampled from original spatial resolution of 30m×30m to 250×250 m using nearest neighbor resampling method (Goyal et al., 2018).

2.3 Research methods 2.3.1 Research Scheme Under the dual impacts of water restriction (rainfall, runoff, and soil loss) and human economic activities on the forest ecosystem, the sustainability of the ecosystem from 2000 to 2015 is assessed using the emergy analysis method (component 1). Then the optimal vegetation coverage under different situation is determined by coupling the emergy analysis and the mountain

Journal Pre-proof eco-hydrological model (component 2 to component 4). The four main components of the research scheme (Figure 2) are discussed in what follows.

Insert Figure 2.

(1) Forest ecosystem assessment from 2000 to 2015 To better understand the current conditions and identify the potential problem of forest ecosystem of Huanjiang County, the sustainability of forest ecosystem is assessed with emergy analysis method with climatic, hydrologic and statistic dataset from 2000 to 2015. More details are provided in section 2.3.2. (2) Simulation of the mountain eco-hydrological process under different situations According to the urban planning document of Huanjiang County (2015-2030) (http://www.hjzf.gov.cn/xxgk/ghjh/20180531-1424382.shtml), the development goal of Huanjiang County will transform from industrial-oriented to tourist-oriented during the short period of 20152020 and long period of 2021-2030. The transformation of development patterns may have impact on GDP, which is associated with the economic feedback into the forest ecosystem. Consider the contradiction between economic development and ecological protection, options should be made as references for the sustainable development maintenance of the forest ecological system under different economic development situations. In this study, we set different economic development levels based on the change of GDP during 2001-2015. Different vegetation coverages were also supposed based on the present vegetation condition. In order to determine the optimal vegetation coverage that is most suitable for the sustainable development of forest ecosystem in Huanjiang County under different economic development level (Figure 3), the gross primary productivity (GPP), runoff and evapotranspiration are simulated using the mountain eco-hydrological model (MECH) under different vegetation coverages as follows: The economic development in Huanjiang County is important for the conservation and proper managements of the forest ecosystem development. Based on the “Five-year Plan” of China, we calculated the annual averaged GDP growth rates of Huanjiang County during 2001-

Journal Pre-proof 2005 (the 10th Five-year Plan), 2006-2010 (the 11th Five-year Plan), and 2011-2015 (the 12th Fiveyear Plan), respectively, and compared with those of entire China (Table 1). The annual increase rate of GDP in China was 8.8%, 11.2%, and 8% during the periods of 2001-2005, 2006-2010, and 2011-2015 respectively, indicating: (i) the transition from high-speed to moderate-high speed growth (Xie, 2007) and (ii) the transition from extensive consumption pattern to resource-saving economic growth pattern (Lang and Zhao, 2013). Similar to the entire country, the GDP growth rate of Huanjiang County was recorded as 9.2%, 17.4%, and 10.7% during the periods of 20012005, 2006-2010, and 2011-2015 respectively. The economic development in Huanjiang County also had transition from low-speed to high-speed, then to moderate-high speed growth. We assume that the economic development mode in the process of urban planning of Huanjiang County is based on the GDP of the 10th, 11th, and 12th Five-year Plan in China (Table1). Therefore, the average GDP in the three periods of “Five-year Plan” are set as the three economic development levels (i.e., D-low, D-moderate, and D-high) for the uncertainty change of GDP in Huanjiang in the future. The average economic feedback into the forest ecosystem of Huanjiang during 20012005, 2006-2010, 2011-2015 was regarded as different background values for economic feedback into the forest ecosystem under different economic development situations.

Insert Figure 3. Insert Table 1. Vegetation coverage may properly reflect the status of vegetation (Li et al., 2014). Studies have shown that the vegetation coverage is positively correlated with soil water content and may reflect the relationship between vegetation growth and soil water content (Wang et al., 2010b, Zhang et al., 2018). Higher vegetation coverage may reduce the total runoff and increase the vegetation biomass through enhancing soil retention (Zuazo et al., 2006). The Normalized Difference Vegetation Index (NDVI) may reflect the growth status of vegetation. With less than 10% of the average error, NDVI can be effectively applied to describe the vegetation coverage (Carlson and Ripley, 1997; Jiang et al., 2006). Therefore, the NDVI is applied to compute the vegetation coverage. And different vegetation coverage situations are set to represent the decrease

Journal Pre-proof from the current of vegetation coverage (in the periods of 2000-2015) by 50%, 40%, 30%, 20%, 10%, and the increase from the current condition by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (i.e., the change of vegetation coverage is from -50% to 100% with the interval of 10%). (3) Calculation of emergy flow under different situations In order to evaluate the sustainability of forest ecosystem, emergy flow is calculated under different vegetation coverage situations in different economic development level. Given that the total runoff, soil loss, forest maintenance costs, labor and biomass are changed with vegetation coverage in different economic development levels, the runoff and biomass are simulated by MECH model, the soil loss is computed by the Revised Universal Soil Loss Equation (RUSLE) (Wang et al., 2000). The forest maintenance costs and labor are assumed proportionally decreased or increased with the change of vegetation coverage and economic development level. As an example, if the vegetation coverage is increased by 50% under a certain economic development level, the costs and labor should be also increased by 50% based on the statistical data in corresponding economic development level. After confirming the values of different input and output elements, each emergy flow can be computed by multiplying UEVs, which introduced in section 2.3.2. (4) Sustainability evaluation of forest ecosystem According to the computation of emergy flow under different situations (different vegetation coverage and economic development level), the comprehensive emergy indices (R/U, ESR, EIR, EYR, ELR and ESI) can be obtained. The optimal vegetation coverage for sustainability of the forest ecosystem of Huanjiang County is investigated based on the range of ESI index. 2.3.2 Emergy analysis method Proposed by Odum (1996), the emery analysis method uses solar energy as the benchmark to measure different types of energy needed in ecological and economic system (Brown and Ulgiati, 2004). It mainly includes the following steps:

Journal Pre-proof (1) Drawing the energy system diagram (Figure 4) to define the structure, the category and the relationships of different components in the forest ecosystem; (2) Identifying the project contents in each input category based on the energy system diagram and the data acquisition, based on the specific situation of the research area; (3) Converting different types of resources (i.e., energy, materials, human services etc.) into emergy through multiplying the Unit Emergy Values (UEVs) that have been corrected according to the baseline 12.1 × 1024 seJ/year listed in Table 2 (Brow and Ulgiati 2016) and detailed emergy calculation is in Appendix A; (4) Compiling the emergy analysis table (Table 2) to summarize the emergy flow, mainly including, total emergy (U), renewable/non-renewable resources (R/N), renewable/non-renewable economic feedback input (EFIr/EFIn), and energy of output resources (O); (5) Computing emergy indices (Table 3), discussing the structure, function and productivity of the ecosystem, and providing decision support for its sustainable development as listed in Table 4 (Jung et al., 2018). Furthermore, the input and output resources are discussed in detail to clearly explain rationale for the selections. Total emergy (U) is the sum of renewable (R) and non-renewable resources (N) from the natural environment as well as economic feedback input (EFI) from human economic activities. R refers to the renewable natural resources in the ecosystem. With the main research objective on vegetation; solar radiation, annual net rainfall amount (the difference between total rainfall and runoff) are used to represent the heat and water conditions required for vegetation growth. N refers to non-renewable natural resources. Due to the impact of terrain, vegetation and other influencing factors, soil loss may occur in rainfall-runoff process. Thus, the emergy of soil loss is applied as the consumption of non-renewable resources for vegetation growth in this study. EFI refers to the economic feedback input obtained from human economic activities and it is mainly composed of renewable economic feedback input (EFIr) and non-renewable economic feedback input (EFIn). In this study, labor and costs are considered as EFIn (i.e., EFI1 and EFI2)

Journal Pre-proof similar to the other studies (Ali et al., 2019; Zhang et al. 2016). EFI1 represents the maintenance costs including infrastructure, sapling, fertilizer, pesticide, farm implement, fuel, electricity. And EFI2 represents the labor. O refers to the resource output from the system. In the forest ecosystem, the increase of biomass is the most obvious manifestation after receiving the external energy and material. Therefore, the live timber reserve is applied as the output resource.

Insert Figure 4 Insert Table 2 Insert Table 3 Insert Table 4

2.3.3 Mountain Eco-hydrological model The mountain eco-hydrological model (MECH) integrates the water cycle, carbon cycle and energy cycle. Compared with the distributed hydrological model (DTVGM, Zou et al., 2017), MECH simultaneously considers the impact of the hydrological process on vegetation and refines the key process. The mountain topography, soil, geology, climate and vertical distribution of vegetation was taken into consideration to simulate the runoff on the slope and energy transmission. Two main modules of MECH are: (1) runoff module that considers infiltration at the downstream, return flow produced by the interflow, and baseflow; (2) radiation transmission module that considers the impacts of mountain terrain (i.e., slope and slope direction) on total solar radiation, which may properly reflect the hydrothermal cycle and energy process of the mountainous ecosystem. (1) Runoff and evapotranspiration Based on the water balance equation, the runoff (R) is expressed as: 𝑅 = 𝑃 ― 𝐸𝑇 ― ΔS

(1)

where: P represents the precipitation; ΔS represents the change of storage which is set to zero since the annual change of storage can be ignored; and ET represents the evapotranspiration which is

Journal Pre-proof computed using Penman-Monteith equation. ET is composed of actual vegetation evaporation (Ec), canopy evapotranspiration (Ei), and actual soil evaporation (Es) as: Δ𝑅𝑛𝑐 +

𝐸𝑐 =

𝜌𝐶𝑝𝐷0 𝑟𝑎𝑐

[ (

𝑟𝑐

𝜌𝑤𝜆 Δ + 𝛾 1 + 𝑟

𝑎𝑐

)]

Δ𝑅𝑛𝑐 +

𝐸𝑖 =

ρ𝐶𝑝𝐷0 𝑟𝑎𝑐

(1b)

𝜌𝑤𝜆(Δ + 𝛾) 𝑊𝑓𝑟

Δ(𝑅𝑛𝑠 ― 𝐺) +

𝐸𝑠 =

(1a)

(1 ― 𝑊𝑓𝑟)

[ (

𝜌𝐶𝑝𝐷0 𝑟𝑎𝑠 𝑟𝑠

𝜌𝑤𝜆 Δ + 𝛾 1 + 𝑟

𝑎𝑠

(1c)

)]

where: λ is latent hear of water vaporization, Δ is the gradient of saturation vapor pressuretemperature curve (kPa/oC), ρ is the air density, 𝜌𝑤 is the water density, Cp is the specific heat of air at constant pressure, G is soil heat flux, γ is psychometric constant (kPa/oC), Wfr is the ratio of wet area, rac is the resistance of canopy surface boundary layer, rs is the resistance of soil, and rc is the resistance of canopy. R𝑛𝑐 and R𝑛𝑠 represent the radiation that reached to canopy and soil (Appendix B). (2) Gross primary productivity (GPP) The Gross primary productivity is related to many factors and may be expressed as: (2)

GPP = 0.48 ∙ LUE ∙ Ft ∙ Fm ∙ Fpar ∙ Rns where: LUE denotes the utilization efficiency of light energy; Ft denotes the thermal stress as: Ft = e

-3 ×

(T 22- 22)

2

(2a)

where: T is the temperature in oC. Fm denotes the soil moisture limiting factor as: (2b)

Fm = 1 - 0.019 ∙ VPD where: VPD is the vapor pressure deficit. Fpar denotes the fraction of photosynthetic active radiation as:

(2c)

𝐹𝑝𝑎𝑟 = 0.95𝐹𝑐𝑜𝑣𝑒𝑟 𝐹𝑐𝑜𝑣𝑒𝑟 = 1 ―

(

𝑁𝐷𝑉𝐼𝑚𝑎𝑥 ― 𝑁𝐷𝑉𝐼 𝑁𝐷𝑉𝐼𝑚𝑎𝑥 ― 𝑁𝐷𝑉𝐼𝑚𝑖𝑛

𝑎

) ; 𝑁𝐷𝑉𝐼

𝑚𝑎𝑥

= 0.9, 𝑁𝐷𝑉𝐼𝑚𝑖𝑛 = 0.1

(2d)

Journal Pre-proof where: a is an empirical constant (set as 0.6), and 𝐹𝑐𝑜𝑣𝑒𝑟 means the vegetation coverage. NDVImax and NDVImin represent the NDVI for canopy and bare land respectively for the research area. Rns is net short wave radiation as: 𝑅𝑛𝑠 = 𝑅𝑛𝑒 ―0.61𝐿𝐴𝐼 𝐿𝐴𝐼 = 2ln (1 ― 𝐹𝑐𝑜𝑣𝑒𝑟)

(2e) (2f)

where: Rn denotes the net radiation at the top of canopy, and LAI denotes the leaf area index.

3. Results 3.1 Model verification Figure 5 shows the comparisons of simulated and observed annual runoff and biomass for the study area. The root mean square error (RMSE) is obtained as 81.65 mm and 13.63 g C/m2 for annual runoff and biomass respectively. The comparison results indicate the MECH model may be properly applied to simulate the annual runoff and biomass.

Insert Figure 5.

With the increase of vegetation coverage, the evapotranspiration (ET) and vegetation biomass (GPP) increase from 491 mm to 808 mm and 1385 g C/m2 to 2544 g C/m2, respectively (Figure 6b and 6c), the runoff and the soil loss decrease from 969 mm to 652 mm and 390 t/km2 to 15 t/km2, respectively (Figure 6a and 6d). The increasing and decreasing rate of the above variables starts to level off with the continuous increase of vegetation coverage. When the vegetation coverage is increased by more than 30%, the soil loss remains at about 15 t/km2.

Insert Figure 6.

3.2 Emergy analysis of forest ecosystem from 2000-2015 3.2.1 Total emergy and its composition

Journal Pre-proof As shown in Table 5, the total emergy (U) input in the forest ecosystem in Huanjiang ranged from 2.64×1020Sej to 3.96×1020Sej. Among all the components, the emergy of renewable natural resource (R: e.g., solar radiation, rainfall, etc.) varied from 1.9×1020Sej to 2.12× 1020Sej. The small fluctuation indicated the emergy of R was not greatly impacted by the climate factor. Since 2000, due to the forest coverage increasing, the emergy of soil loss (N) showed a decreasing tendency within the range of [2.26×1019Sej, 4.95×1019Sej]. The emergy of economic feedback input (EFI) increased from 2.92×1019Sej to 16.4×1019Sej. The ratio of R (emergy of renewable resources) to U (total emergy) presented a decreasing trend since 2009 (Table 6), with an average annual value of 69.8%. The change of R/U ratio indicated that (i) the renewable resources was the main source of the total emergy in the forest ecosystem before 2009, and (ii) the sustainability of forest ecosystem decreased gradually. The R/U ratio was found less than 50% by 2015, and the research area was identified as transition stage (as listed in Table 4). The ratio of EFI (emergy of feedback input by economic activities) to total emergy (U) varied from 9.8% to 41.5% (Table 6). EFI/U was less than 20% before 2011, and reached its maximum in 2015. Before 2007, human labor (EFI2) was the dominated factor impact the EFI. Since 2007, the input of vegetation conservation exceeded labor input and became the main source of EFI. The average emergy transformity of output resource (O: live timber reserve) fluctuated along 4.51×104Sej/J, with the average live timber reserve of 6.63×1015J and average total emergy (U) of 2.94 ×1020Sej (Table 5). With the substantial increase in EFI, the transformity of live timber reserve was observed to continually increase after 2011. Small change was found in the emergy of renewable/non-renewable natural resources among three periods (Figure 7). Although the EFI increasing was benefit for the ecosystem output (i.e., increase of live timber reserve), the increase rate of EFI was larger than that in live timber reserve. Compared with 2000-2005, the emergy of EFI was increased by 9.45×1018Sej in 20062010, and the live timber reserve was increased by 1.27×105m3. Compared with 2006-2010, the emergy of EFI was increased by 6.62×1019Sej from 2011-2015, however the live timber reserve was only increased by 4.5×105m3. This comparison showed that when the EFI increased by about

Journal Pre-proof 7 times, the corresponding live timber reserve only increased by about 3.5 times. Therefore, expanding live timber reserve may increase the pressure of human economic input.

Insert Figure 7 Insert Table 5 Insert Table 6

3.2.2 Emergy indices analysis From 2000-2015, the annual average emergy self-support ratio (ESR) was 82.5% with the minimum value of 58.5% in 2015 (Table 6). Compared with the developed countries and regions, e.g., Switzerland (19%, Yan and Odum, 1998), Beijing (13.14%, Zhou et al., 2006) and Guangzhou (47%, Dong et al., 2007), the utilization efficiency of natural resources is relatively high for the forest ecosystem of Huanjiang County. EIR (i.e., the ratio of emergy of economic feedback input to natural resources in the system) is an important indictor to evaluate whether the system may sufficiently utilize the human economic input for system development (Brown and Ulgiati, 1997). From 2000 to 2015, the EIR of Huanjiang County increased from 0.11 to 0.71, which was significantly lower than that of Baotou (3.26 (2004), Liu et al., 2009b) and Hongkong (1.25 (1988), Lan and Odum, 1994). EYR (i.e., the emergy yield ratio) measures the ability of developing local natural resources in the system development (Zhang and Qiu, 2018). EYR decreased from 10.21 to 2.41 during the period of 2000 to 2015, indicating the state of forest ecosystem gradually transformed from transitional to unsustainable. Although forest ecosystem’s ability to utilize natural resources was gradually declined, EYR of Huanjiang was still higher than that of Beijing and Shanghai (Liu et al., 2009b). ELR (i.e., environmental loading ratio) is an indicator of the pressure on the environment caused by the system development. There has been small inter-annual change in soil loss, but significant increase in economic feedback input. Before 2014, ELR was found less than 1, indicating that the development of forest ecosystem consumed more renewable resources than

Journal Pre-proof non-renewable resources. Although the ELR reached its maximum value (1.09) in 2015; it was still lower than that in Hangzhou (34.9, Liu et al., 2009b) and Kunming (5.33, Liu et al., 2009b). Therefore, compared with the developed regions, the environmental pressure was relatively low in the forest ecosystem of Huanjiang County. ESI (i.e., the ratio of EYR to ELR) is an indicator of the system sustainability. A relative high EYR and a relative low ELR indicate high output of the system and low pressure on the environment (Brown and Ulgiati, 1997). Table 6 showed that ESI decreased continuously from 23.83 in 2000 to 2.22 in 2015, with a rate of 2.03/year, indicating the state of forest ecosystem gradually changed from sustainable to transition. The sustainability of the forest ecosystem continuously decreased from 2000 to 2015 with the minimum ESI (2.22) obtained in 2015. When the EYR/ELR approaches 1.0 and the system “straddles” the transition state between sustainability and unsustainability. Thus, it is important to find an optimal environmental condition for the future sustainability of the ecosystem.

3.3 Optimal situation selection of forest ecosystem In order to evaluate the sustainability of ecosystem during the future long-term urban planning, MECH model was introduced in the emergy analysis. The sustainable development of Huanjiang forest ecosystem is analyzed with the MECH model and emergy analysis under different situations (i.e., different economic development levels and different vegetation coverages) which are shown in Figures 8-9. With the increasing of vegetation coverage, the biomass of the forest ecosystem increases while the consumption of non-renewable resources decreases (Figure 8a). The biomass and economic feedback input (EFI) show an exponential relationship (Figure 8b), indicating the continuous increase of the energy or material input from human economic activities may cause pressure on the system development.

Insert Figure 8.

Journal Pre-proof As shown in Figure 9, the economic development level was found to be negatively correlated with renewability ratio (R/U) and emergy self-support ratio (ESR), i.e., high economic development yielded low R/U and ESR under a given vegetation coverage. These phenomena indicated that the increasing human economic activity enhanced the pressure on the development of forest ecosystem. Similarly, EYR (Figure 9d) and ESI (Figure 9f) were also negatively correlated with economic development level under a given vegetation coverage. EIR (Figure 9c) was found to be positively correlated with the economic development level; indicating that the higher level of economic development brought more economic feedback input to the system. However, the increasing of economic feedback input may also increase the pressure on environment (Figure 9e). To this end, the accelerated economic development may bring substantial pressure on the ecosystem development, which is not conductive to the healthy and sustainable development of the system. With the increasing of vegetation coverage, R/U (Figure 9a) showed a rapid upward trend first, then a slow decreasing tendency, which was related with the change of runoff and soil loss. Furthermore, the utilization of renewable energy reached its maximum value when the vegetation coverage was increased by 30-40%. Because the soil loss was correlated with vegetation coverage, the increasing of vegetation coverage lead to soil retention increasing, resulting in the reduction of soil consumption. Compared with the current vegetation coverage, the consumption of natural resources (soil) decreased with vegetation coverage increasing (Figure 9b), which is consistent with the change of EYR shown in Figure 9d. More economic feedback input emergy was needed for the development of the ecosystem with vegetation coverage increasing (Figure 9c). When less renewable natural resources were used, higher pressure was detected on system development (Figure 9e). With the reduction of soil loss tended to a constant, the increasing economic feedback input started to manifest itself. The utilization of economic feedback input increased when the vegetation coverage increased by 30%40%. If the vegetation coverage reduced by 50%; both emergy yield ratio (EYR) and environmental loading ratio (ELR) were found to reach their maximum value. Therefore, although not all the emergy indices were optimal under the same vegetation coverage, Huanjiang forest

Journal Pre-proof ecosystem achieved its optimal sustainable state when vegetation coverage increases by 30%, 20%, and 10% when the GDP value of the county in the future as that in the 10th, 11th, and 12th Fiveyear Plan in China, respectively (Figure 9f).

Insert Figure 9.

4. Discussions 4.1 Relationship between sustainability and emergy components EYR and ELR are the two important factors impact emergy sustainablility index (ESI) and can be re-written as: U

EYR = EFI = ELR =

R + N + EFI EFI

N + EFI R

N

=R+

R

N

= EFI + EFI +1

𝐸𝐹𝐼 𝑅

N/EFI

1

= R/EFI + R/EFI

(3) (4)

R/EFI and N/EFI indicate the utilization of renewable and non-renewable resources for the system development under same economic development level. The renewable resources (R), non-renewable resources (N) and economic feedback input (EFI) are important for sustainable development of the system (eq.3 and eq.4). From 2000 to 2015, the input of the renewable resources did not change significantly. However, the economic feedback input was greatly increased with GDP increasing. With the increase of vegetation coverage in Huanjiang forest ecosystem, the soil conservation is improved and the runoff decreases. With the increase of economic feedback input in the forest ecosystem, the pressure for system development is also increased (Chen et al., 2017). Therefore, both R/EFI and N/EFI show a descending trend with respect to change of vegetation coverage for all economic development levels (Figure 10). The optimal situation for the system sustainable development is to maximize the utilization of renewable resource and minimize the consumption of non-renewable resources. However, when the utilization of renewable natural resources is maximized, the consumption of non-renewable resources also reaches its maximum (Figure 10). If setting the minimum consumption of non-renewable resources as the benchmark

Journal Pre-proof for the sustainable development without considering the utilization of renewable resources, the best option is to increase vegetation coverage by 100%. But one cannot ignore that the increase in vegetation coverage is accompanied by the increase in economic feedback input, which may result in bigger pressure on system development. The trend of ESI in Huanjiang forest ecosystem shows a negative relationship with EFI. Therefore, it is impossible to optimize all the indices at the same time. The higher the economic development level and vegetation coverage are, the higher the economic feedback input is. Under the same vegetation coverage situation, high economic development level imposes greater pressure on the system than the low level of economic development does. Therefore, compared with the low economic development level, appropriate reduction of vegetation coverage is benefit for the overall sustainability of the system under high economic development level. In summary, with the sustainability identified as high EYR and low ELR, the forest ecosystem reaches to the optimal sustainability stage if the vegetation coverage increased by 30%, 20% and 10% when the GDP value of the county in the future as those in the 10th, 11th, and 12th Five-year Plan in China.

Insert Figure 10.

4.2 The relationship between economic development and vegetation coverage Generally, the economic development may lead to biodiversity decline, soil erosion, and land degradation, hence it may cause vegetation degradation. For instance, vegetation coverage was negative correlated with the economy development, which was found in some cities along the Beijing-Guangdong railway (Liu et al., 2018), while it was found to be positively related to economy development in Rearl River Delta (Hu and Xia, 2019). Protective measures and investments (subsidy for farmers: the Grain for Green program and purchase for saplings) into forest ecosystems from government improved the vegetation coverage to a certain extent (Chen et al., 2015). It is worth to note that, large investment on the restoration project does not necessarily lead to high project effectiveness. And the project effectiveness is also affected by factors, such as climate, topography and human management (Tong et al., 2017).

Journal Pre-proof The annual decline of EYR in the forest ecosystem in Huanjiang County may be related to the temperature rising and poor human management. The GDP of Huanjiang County mainly depends on the primary industry and tertiary industry. The vegetation coverage of Huanjiang forest ecosystem showed the low positive correlation with GDP (r=0.39, p>0.05) from 2000 to 2015. The low correlation indicates that the economic development of Huanjiang County may promote the vegetation growth; however, the higher the economic development level is, the more external input and corresponding better human management are needed. Moreover, the high economic development level may enhance the pressure on the sustainable development of forest ecosystem. Therefore, the optimal vegetation coverage and ESI of forest ecosystem under high economic development is lower than that under low economic development.

5. Conclusion Using the meteorological, hydrological, vegetation, and economic statistical data of Huanjiang County, the sustainable development characteristics of Huanjiang forest ecosystem from 20002015 was analyzed with emergy analysis method. By coupling the emergy analysis with the mountain eco-hydrological model, the most suitable vegetation coverage for the forest ecosystem under different economic development levels was investigated. The total emergy supporting the forest ecosystem of Huanjiang County was estimated in the range of [2.64×1020Sej, 3.96×1020Sej] from 2000-2015, in which 69.8% was renewable natural resources. With the increase in labor and maintenance cost, the emergy of economic feedback input increased and enhanced the pressure on the development of ecosystem. With the decrease of EYR and increase of ELR, the forest ecosystem shifted toward unsustainability. The simulation results of emergy analysis and mountain eco-hydrological model indicate that both the economic feedback input and the pressure on the system development increase with the increasing of economic development. Five investigated emergy indices (i.e., R/U, ESR, EIR, EYR, ELR) may not reach the optimal values under the same vegetation coverage. Thus, based on the change of ESI, vegetation coverage increased by 30%, 20% and 10% were suggested for sustainable

Journal Pre-proof development of the forest ecosystem if the GDP value of the county in the future as those in the 10th, 11th, and 12th Five-year Plan in China. The increasing environment loading ratio (ELR), decreasing renewability (R/U) and relative low sustainability of the forest ecosystem are mainly caused by large economic feedback input. Due to the low use efficiency of economic feedback, the accumulation of vegetation biomass increased insignificantly. In order to increase the use efficient of resources, the economic feedback should be reasonable allocated in different aspects, such as the supply of purchased materials, salary of forestry worker and the construction of soil protection measures. In addition, in forest conservation, the use of chemical fertilizers should be reduced, the renewable and cleaner energy should be encouraged, for example, by using solar energy and biogas digester to replace the need for burning wood in people’s daily life. The assessment of the forest ecosystem sustainability based on emergy analysis may help to understand the relationship between human economic activities and ecological environment. However, there are still some limitations in this study. Firstly, the non-uniqueness of UEVs for different kinds of energy or material flows may lead to the discrepancy comparing with other researches. Secondly, the accuracy of the MECH model in simulating the ecological and hydrological elements remains to be improved for the special geological environment of karst mountainous region. Thirdly, this study assumes the linear relationship between the economic feedback input and vegetation coverage, however the nonlinear relationship may be observed in the actual forest conservation process and remains to be investigated. In summary, the further research direction on the assessment of the ecosystem is how to better integrate the emergy analysis method with the ecosystem models.

Acknowledgement: The research was supported by the National Basic Research Program of China (973 Program, No. 2015CB452701), Natural Science Foundation of China (NSFC) grants (41971232) and Geology and Mineral Resources Survey Project: Ecological Configuration and Global Strategy of China Water Resources (DD20190652)

Journal Pre-proof Appendix A: Emergy calculation process

Insert appendix Table A

B: Calculation of R𝑛𝑐 and R𝑛𝑠 𝑅𝑛𝑐 = 𝑅𝑠𝑐() + 𝑅𝑙𝑐() ― 𝑅𝑠𝑐𝑟() ― 𝑅𝑙𝑐𝑎() ― 𝑅𝑙𝑐𝑠() + 𝑅𝑙𝑠𝑐()

(B-1)

𝑅𝑛𝑠 = 𝑅𝑠𝑠() + 𝑅𝑙𝑠() ― 𝑅𝑠𝑠𝑟() ― 𝑅𝑙𝑠𝑐𝑎() + 𝑅𝑙𝑐𝑠()

(B-2)

where: 𝑅𝑠𝑐 is the downward solar short wave radiation, 𝑅𝑙𝑐 is the downward long wave radiation, 𝑅𝑠𝑐𝑟 is the upward short wave radiation from the canopy, 𝑅𝑙𝑐𝑎 is the long wave radiation from the canopy to the sky, 𝑅𝑙𝑐𝑠 is the long wave radiation from the canopy to the surface, 𝑅𝑙𝑠𝑐 is the upward long wave radiation from the surface. 𝑅𝑠𝑠 is the downward remaining solar short wave radiation from canopy, 𝑅𝑙𝑠 is the long wave radiation from the sky, 𝑅𝑠𝑠𝑟 is the upward short wave radiation from the surface, 𝑅𝑙𝑠𝑐𝑎 is the upward long wave radiation from the surface. And they are calculated as following:

[

𝑅𝑠𝑐 = 𝑅𝑠 1 ― exp

(

― 𝐾𝑠𝐿𝐴𝐼

)]

(2cos )0.5

(B-3)

𝑅𝑙𝑐 = 𝑅𝑙[1 ― exp ( ― 𝐾𝑙𝐿𝐴𝐼)]

(B-4)

𝑅𝑠𝑐𝑟 = 𝑐𝑅𝑠𝑐

(B-5)

𝑅𝑙𝑐𝑎 = 𝑅𝑙𝑐𝑠 = 𝑐𝑇4𝑐 𝐹𝑐𝑜𝑣𝑒𝑟 𝑅𝑙𝑠𝑐 = 𝑐𝑇4𝑔𝑠𝐹𝑐𝑜𝑣𝑒𝑟 𝑅𝑠𝑠 = 𝑅𝑠exp

(

― 𝐾𝑠𝐿𝐴𝐼

(B-6) (B-7)

)

(2cos )0.5

𝑅𝑙𝑠 = 𝑅𝑙exp ( ― 𝐾𝑙𝐿𝐴𝐼) 𝑅𝑠𝑠𝑟 = 𝑠𝑅𝑠𝑠 𝑅𝑙𝑠𝑎𝑐 = 𝑠𝑇4𝑔𝑠

(B-8) (B-9) (B-10) (B-11)

where: 𝐾𝑠 and 𝐾𝑙 are the attenuation coefficient of canopy to short and long wave radiation, respectively. 𝐿𝐴𝐼 is leaf area index,  is the angular position of the sun at solar noon with respect to the plane of the equator, 𝑐 and 𝑠 are the short wave radiation albedo of canopy and surface, respectively. 𝑙 and 𝑠 are long wave radiation emissivity of canopy and surface,

Journal Pre-proof respectively.  is the constant of Stefan-Boltzmann. 𝑇𝑐 and 𝑇𝑔𝑠 are the temperature of canopy and ground surface, respectively. 𝐹𝑐𝑜𝑣𝑒𝑟 is vegetation coverage. The impacts of mountain terrain (i.e., slope and slope direction) on total solar radiation may be evaluated using: 𝐾𝑏ℎ𝑜𝑟

𝐾𝑑ℎ𝑜𝑟

(B-12)

𝑅𝑠 = 𝑅𝑠𝑚ℎ𝑜𝑟(𝑓𝐵𝜏𝑠𝑤ℎ𝑜𝑟 + 𝑓𝑖𝑎𝜏𝑠𝑤ℎ𝑜𝑟 +𝛼(1 ― 𝑓𝑖))

where: 𝑅𝑠𝑚ℎ𝑜𝑟 is the measured global solar radiation on a horizontal surface, 𝑓𝐵 is the ratio of expected direct radiation on the slope to the direct radiation on the horizontal surface. 𝛼 is vegetation coefficient. For the grass land and deciduous broad-leaved forest, 𝛼 is in the range of [0.15,0.25]. For evergreen coniferous forest, 𝛼 is in the range of [0.1, 0.15]. For open water, 𝛼 is in the range of [0.04, 0.08], and for bare soil, the range of 𝛼 is 0.15–0.35. 𝑓i and 𝑓ia are the scale factors about slope, if the diffuse radiation component is considered to behave isotropically, 𝑓i = 𝑓ia. Otherwise, 𝑓i and 𝑓ia are calculated as: (B-13)

𝑓𝑖 = 0.75 + 0.25cos (𝑠) ―0.5𝑠/𝜋

( (

𝑓𝑖𝑎 = (1 ― 𝐾𝑏ℎ𝑜𝑟) × 1 +

𝐾𝑏ℎ𝑜𝑟

)sin ( ))𝑓 + 𝑓 𝐾

𝐾𝑏ℎ𝑜𝑟 + 𝐾𝑑ℎ𝑜𝑟

3 𝑠 2

𝑖

𝐵 𝑏ℎ𝑜𝑟

(B-14)

where: s is the surface slope. 𝐾𝑑ℎ𝑜𝑟 is the index for actual diffuse radiation on the horizontal surface as: 𝐾𝑑ℎ𝑜𝑟 = 𝜏𝑠𝑤ℎ𝑜𝑟 ― 𝐾𝑏ℎ𝑜𝑟

(B-15)

𝐾𝑏ℎ𝑜𝑟 is transmissivity index for actual direct radiation on the horizontal surface and calculated as:

{

1.56𝜏𝑠𝑤ℎ𝑜𝑟 ― 0.55 𝜏𝑠𝑤ℎ𝑜𝑟 ≥ 0.42 2 3 0.022 ― 0.280𝜏 + 0.828𝜏 + 0.765𝜏 0.175 < 𝜏 𝐾𝑏ℎ𝑜𝑟 = 𝑠𝑤ℎ𝑜𝑟 𝑠𝑤ℎ𝑜𝑟 𝑠𝑤ℎ𝑜𝑟 𝑠𝑤ℎ𝑜𝑟 < 0.42 0.016𝜏𝑠𝑤ℎ𝑜𝑟 𝜏𝑠𝑤ℎ𝑜𝑟 ≤ 0.175 (B-16) 𝜏𝑠𝑤ℎ𝑜𝑟 is atmospheric transmittance for the horizontal surface as: 𝜏𝑠𝑤ℎ𝑜𝑟 =

𝑅𝑠𝑚ℎ𝑜𝑟 𝑅𝑎ℎ𝑜𝑟

(B-17)

where: 𝑅𝑎ℎ𝑜𝑟 represents the extraterrestrial radiation on the slope and horizontal surface.

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Journal Pre-proof Zuazo, V.H.D., Martínez, J.R.F., Pleguezuelo, C.R.R., Raya, A.M., Rodríguez, B.C., 2006. Soilerosion and runoff prevention by plant covers in a mountainous area (se spain): Implications for sustainable agriculture. Environmentalist 26, 309-319.

Journal Pre-proof Author Contribution Statement Zhan Chesheng: Conceptualization, Methodology, Funding acquisition, Writing - Review & Editing Zhao Ruxin: Investigation, Formal analysis Hu Shi: Software, Data curation, Writing- Original draft preparation.

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Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1 Location, elevation and land use of Huanjiang County. Note: EBF, DBF, ECF, MF, Shrub, GL, DL, PL, Other are short for evergreen broad-leaf forest, deciduous broad leaved forest, Evergreen coniferous forest, mixed broadleaf-conifer forest, shrub land, grass land, dry land, paddy land and other type of land, respectively

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Figure 2 The research scheme.

Figure 3 Design situation and main objective.

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Figure 4 Summary diagram of the emergy flows in forest ecosystem

Figure 5 Comparisons of the simulated and the observed runoff and biomass (a) runoff validation, (b) biomass validation

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Figure 6 Variation of different elements under different vegetation coverage

Figure 7 Interdecadal changes of component flows in forest ecosystem of Huanjiang County

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Figure 8 Relationship between simulated output and different emergies. (a): simulated output v.s. change of vegetion coverage and non-renewable resources (b): simulated output v.s. economic feedback input

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Figure 9 The curve of emergy index under different situations

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Figure 10 The ratio of renewable and non-renewable natural resources utilization to economic feedback input (a) low economic development, (b)moderate economic development and (c)high economic development

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The sustainability of forest ecosystem in Huanjiang County has been assessed with emergy analysis.



An approach combined emergy analysis and eco-hydrological model is proposed.



To enhance the management ability of economic feedback will improve the ecosystem sustainability.



The optimal vegetation coverage of forest ecosystem in Huanjiang County is assessed.

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Table 1 Changes of GDP of China and Huanjiang county Average GDP/a hundred million yuan Annual growth rate of GDP /% Average GDP/a hundred million yuan Annual growth rate of GDP /%

China Huanjiang County 2001-2005 is the period of 12th Five-year Plan)

10th

2001-2005

2006-2010

134,973

309,455

578,815

8.8

11.2

8

12.1

24.9

37.3

9.2

17.4

10.7

Five-year Plan, 2006-2010 is the period of

11th

2011-2015

Five-year Plan, and 2011-2015 is the period of

Table 2 Detail information about emergy flow in forest ecosystem of Huanjiang County Emergy category Total emergy (U=R+N+EFI) Renewable resources (R) R1 Solar radiation R2 Rain-chemical Non-renewable resources (N) N1 Soil loss Economic feedback input (EFI=EFIr+EFIn) Renewable economic feedback input (EFIr) —— Non-renewable economic feedback input (EFIn) EFI1 Maintenance costs including forest infrastructure, sapling, fertilizer, pesticide, farm implement, fuel, electricity EFI2 Labor Energy of output resources (O) O1 Live timber reserves

Indices Transformity Renewability Emergy self-support ratio

Emergy investment ratio

Emergy yield ratio

Environment loading ratio

Emergy sustainability index

Units

UEVs (Sej/unit)

Reference

J J

1.00 2.31×104

(Ali et al., 2019)

J

9.4×104

(Ali et al., 2019)

$

1.92×1012

(Zhai et al., 2018)

J

4.86×105

(Zhai et al., 2018)

m3

——

Table 3 Index system of emergy analysis Formulas Description The ratio of the total emergy input to the energy of Tr=U/O the output (Sej/J) (Hu et al., 2010). The ratio of the renewable natural resources to the R/U total emergy of the system. High ratio yields high system sustainability (Zhang et al., 2018). The ratio of the natural resources to the total emergy, ESR=(R+N)/U representing the system's utilization efficiency of natural resources (Zhang et al., 2018). The ratio of input emergy of economic feedback to the emergy of natural resources, an indicator to make EIR=EFI/(R+N) full use of external inputs in the development of a system (Ohnishi et al., 2017). The ratio of total emergy to the input emergy of economic feedback, indicating whether the natural EYR=U/EFI resources (renewable/non-renewable) may be fully exploited (Ohnishi et al., 2017). The ratio of non-renewable emergy (including nonrenewable natural resources and non-renewable ELR=(N+EFIn)/(R+EFIr) economic feedback) to the renewable emergy. High ratio indicates high environmental pressure (Zhang and Qiu, 2018). The ratio of EYR to ELR, indicating the ESI=EYR/ELR sustainability of a system (Hu et al., 2010).

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Table 4 Relation between emergy indices and sustainability 　

EYR

ELR

ESI

Sustainable

>15

<2

>5

Transitional

4-15

2-10

1-5

Unsustainable

<4

>10

<1

Table 5 The major emergy flow of Huanjiang County from 2000 to 2015 Item The total emergy, U

2000

2001

2002

2003

2004

2005

2006

2007

sej

2.76E+20

2.67E+20

2.68E+20

2.66E+20

2.64E+20

2.82E+20

2.78E+20

2.70E+20

Renewable resources, R

sej

1.98E+20

2.02E+20

2.01E+20

2.08E+20

2.06E+20

2.06E+20

2.06E+20

2.01E+20

R1 Sunlight

sej

1.18E+19

1.17E+19

1.20E+19

1.30E+19

1.25E+19

1.19E+19

1.22E+19

1.27E+19

R2 The difference of rainfall and runoff, chemical

sej

1.86E+20

1.90E+20

1.89E+20

1.95E+20

1.94E+20

1.94E+20

1.93E+20

1.89E+20

sej

4.95E+19

3.54E+19

4.08E+19

3.02E+19

2.81E+19

4.77E+19

4.38E+19

3.79E+19

sej

4.95E+19

3.54E+19

4.08E+19

3.02E+19

2.81E+19

4.77E+19

4.38E+19

3.79E+19

Economic feedback input, EFI

sej

2.92E+19

2.99E+19

2.62E+19

2.76E+19

2.94E+19

2.80E+19

2.87E+19

3.05E+19

EFI1 Maintenance costs

sej

3.01E+18

3.34E+18

4.05E+18

4.74E+18

5.79E+18

3.29E+18

4.85E+18

8.17E+18

EFI2 Human labor

sej

2.62E+19

2.66E+19

2.22E+19

2.29E+19

2.36E+19

2.47E+19

2.39E+19

2.23E+19

J

6.19E+15

6.94E+15

6.72E+15

6.63E+15

7.81E+15

4.82E+15

5.06E+15

7.11E+15

J

6.19E+15

6.94E+15

6.72E+15

6.63E+15

7.81E+15

4.82E+15

5.06E+15

7.11E+15

Sej/J

4.46E+04

3.85E+04

3.99E+04

4.01E+04

3.38E+04

5.85E+04

5.49E+04

3.79E+04

2008

2009

2010

2011

2012

2013

2014

2015

Nonrenewable recources, N N1 Soil loss

Output, O O1 Live timber reserves Transformity Item The total emergy, U

sej

2.83E+20

2.79E+20

2.83E+20

2.86E+20

3.19E+20

3.34E+20

3.60E+20

3.96E+20

Renewable resources, R

sej

1.96E+20

2.12E+20

2.06E+20

2.07E+20

2.00E+20

2.04E+20

1.98E+20

1.90E+20

R1 Sunlight

sej

1.20E+19

1.33E+19

1.26E+19

1.31E+19

1.08E+19

1.26E+19

1.12E+19

1.10E+19

R2 The difference of rainfall and runoff, chemical

sej

1.84E+20

1.99E+20

1.93E+20

1.94E+20

1.90E+20

1.91E+20

1.87E+20

1.79E+20

sej

4.53E+19

2.71E+19

2.96E+19

2.26E+19

4.37E+19

3.05E+19

3.98E+19

4.16E+19

sej

4.53E+19

2.71E+19

2.96E+19

2.26E+19

4.37E+19

3.05E+19

3.98E+19

4.16E+19

Economic feedback input, EFI

sej

4.21E+19

4.02E+19

4.74E+19

5.71E+19

7.50E+19

9.91E+19

1.22E+20

1.64E+20

EFI1 Maintenance costs

sej

1.64E+19

1.70E+19

2.43E+19

3.38E+19

5.10E+19

7.49E+19

9.82E+19

1.40E+20

EFI2 Human labor

sej

2.57E+19

2.32E+19

2.32E+19

2.33E+19

2.40E+19

2.42E+19

2.42E+19

2.43E+19

Output, O

J

6.26E+15

7.64E+15

6.85E+15

7.55E+15

6.56E+15

6.99E+15

6.30E+15

6.68E+15

O1 Live timber reserves Transformity (Sej/J)

J

6.26E+15 4.52E+04

7.64E+15 3.66E+04

6.85E+15 4.13E+04

7.55E+15 3.79E+04

6.56E+15 4.87E+04

6.99E+15 4.77E+04

6.30E+15 5.71E+04

6.68E+15 5.92E+04

Nonrenewable recources, N N1 Soil loss

Sej/J

Table 6 Emergy comprehensive indices of Huanjiang County from 2000 to 2015 Item

2000

2001

2002

2003

2004

2005

2006

2007

EFI/U

10.6%

11.2%

9.8%

10.4%

11.1%

9.9%

10.3%

11.3%

R/U

71.5%

75.6%

75.0%

78.3%

78.2%

73.2%

73.9%

74.7%

ESR

89.4%

88.8%

90.2%

89.6%

88.9%

90.1%

89.7%

88.7%

EIR

0.12

0.13

0.11

0.12

0.13

0.11

0.12

0.13

EYR

9.48

8.93

10.21

9.63

8.98

10.07

9.68

8.85

ELR

0.40

0.32

0.33

0.28

0.28

0.37

0.35

0.34

ESI

23.83

27.63

30.62

34.65

32.26

27.44

27.44

26.09

Term

2008

2009

2010

2011

2012

2013

2014

2015

EFI/U

14.9%

14.4%

16.8%

19.9%

23.5%

29.7%

34.0%

41.5%

R/U

69.1%

75.9%

72.8%

72.2%

62.8%

61.1%

55.0%

47.9%

ESR

85.1%

85.6%

83.2%

80.1%

76.5%

70.3%

66.0%

58.5%

EIR

0.17

0.17

0.20

0.25

0.31

0.42

0.51

0.71

EYR

6.71

6.95

5.96

5.01

4.25

3.37

2.94

2.41

ELR

0.45

0.32

0.37

0.39

0.59

0.64

0.82

1.09

ESI

15.02

21.90

15.91

13.01

7.19

5.30

3.59

2.22

Appendix Table A Emergy calculation process Items

Calculation method

Solar radiant emergy The difference of rainfall and runoff, chemical

Land area(m2)×solar radiantion (J/m2)×(1-albedo)×UEVs Land area (m2)×(rainfall-runoff) (mm)×Gibbs free energy(4.94J/g)×the density of water(106g/m3)×UEVs Land area (m2)×the rate of soil loss (kg/m2)×organic content (6%)×heat value of soil organic matter (2.26×107J/kg)×UEVs The costs of investment in forest infrastructure, sapling, fertilizer, fuel, pesticide and electric ($)×UEVs Numbers of personal×average working hours(8h)/24h×365days× calories per person(2500kcal/person)×4186J/kcal×UEVs Volume (m3)×growth rate (0.0378)×density(526kg/m3)×heat of combustion (20090J/g) The ratio of total emergy of a system to the energy of output

Soil loss Forest maintenance cost Human labor Energy of live timber reserves Transformity of output