Journal of Geographical Sciences >
An influencing mechanism for ecological asset gains and losses and its optimization and promotion pathways in China
Li Jiahui (1997-), Master Candidate, specialized in remote sensing of ecology and GIS. E-mail: lijh.19s@igsnrr.ac.cn |
Received date: 2022-05-16
Accepted date: 2022-06-27
Online published: 2022-12-25
Supported by
The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23020202)
Accounting for the gains and losses of ecological assets holds scientific significance in sustaining human well-being. Based on related research on ecological assets, we established a county-scale ecological asset accounting technology system by analyzing the temporal and spatial variations of county-level ecological assets in China from 1990 to 2018 and clarified the factors which caused the gains and losses of ecological assets. On these bases, optimization and promotion pathways were proposed. The results show that the number of counties dominated by farmland and forest ecological resources accounted for about 45% and 37% of the total counties, respectively. From 1990 to 2018, the quality of county-level ecological stock assets showed an increasing trend, while the water conservation volume decreased in nearly 70% of the counties. The number of counties with the gains (47%) and losses (37%) of ecological flow assets demonstrated spatial patterns which showed the same segmentation characteristics as the “Hu Huanyong Line”, that is, the counties in the vastness of northwest China experienced significant gains, while decreases were widespread in eastern and southern China. The change of ecological assets in more than 70% of the counties was driven by climate change and human activities. The average degree of impact of human activities driving the ecological asset gains in counties was about 80%, while that of climate change causing the ecological asset losses was about 60%. According to various ecological resource types, gain and loss status, and its driving factors, counties in China can be classified into five types: climate change mitigation, climate change adaptation, ecological resources restoration, ecological resources protection, and ecological resources management. Our results indicate that differentiated optimization and promotion pathways can be adopted to achieve desired ecological asset gains.
LI Jiahui , HUANG Lin , CAO Wei . An influencing mechanism for ecological asset gains and losses and its optimization and promotion pathways in China[J]. Journal of Geographical Sciences, 2022 , 32(10) : 1867 -1885 . DOI: 10.1007/s11442-022-2027-0
Table 1 Indexes and methods of ecological asset accounting at the county level |
Asset category | Primary indicator | Secondary indicator | Accounting method | Indicator and parameter description |
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Stock assets | Area of ecological resource assets | Asset types | Ranking of the proportional area of forest, farmland, grassland, wetland, and desert ecological resource | Category I: Farmland, farmland-forest-wetland, farmland-forest-grassland, farmland-forest, farmland-grassland, farmland-wetland; Category II: Forest, forest-farmland-grassland, forest-farmland, forest-grassland; Category III: Grassland, grassland-farmland, grassland-forest, grassland-desert; Category IV: Desert; Category V: Others |
Quality of ecological resources | Quality index | $E{{Q}_{ij}}=\frac{NP{{P}_{ij}}}{MNP{{P}_{i}}}$ $EAQ{{I}_{k}}=\underset{i=1}{\overset{m}{\mathop \sum }}\,\frac{E{{Q}_{ij}}}{{{S}_{ki}}}\times \frac{{{S}_{ki}}}{{{S}_{k}}}$ | EQij is the ecological asset quality of the j-th pixel in the i-th ecosystem asset type, and NPPij is the NPP (kgC·ha-1) of it. MNPPi is the highest NPP nationwide of the i-th type ecological asset (kgC·ha-1). EAQIk is the ecological asset quality index of the k-th county. Ski is the area of the i-th type of ecological asset in the k-th county (ha). Sk is the total area of the ecological assets in the k-th county (ha). | |
Flow assets | Supply services | Food supply | ${{V}_{FS}}=FS\times {{P}_{f}}$ | FS is the grain outputs (t·a-1). Pf is the unit price, and the average selling price of rice, wheat, and corn is 2194 yuan·t-1. |
Raw material supply | ${{V}_{PS}}=\underset{j=1}{\overset{m}{\mathop \sum }}\,P{{S}_{j}}\times {{P}_{pj}}$ | PSj is the outputs of the j-th product (t·a-1), and Ppj is its unit price. The average selling prices of cotton and oil crops are 14,564 yuan·t-1 and 6452 yuan·t-1, respectively. | ||
Regulation services | Sedimentation reduction | $SC=({{M}_{sc}}-{{M}_{sce}})\times A$ ${{V}_{SC}}=SC/{{\rho }_{s}}\times {{P}_{SC}}$ | SC is the soil conservation amount (t·a-1). Msc is the soil water erosion amount under the actual surface cover condition (t·ha-1·a-1), while Msce is the soil water erosion amount under the extremely degraded state (t·ha-1·a-1). A is the area of the ecosystem (ha); ρs is the soil bulk density (t·m-3) (Han et al., 2016). PSC is the excavation cost per unit area, which is 12.6 yuan·m-3 (DB11/T 659-2018). | |
Dust pollution reduction | $SF=({{M}_{sf}}-{{M}_{sfe}})\times A$ ${{V}_{SF}}=SF\times {{P}_{SF}}$ | SF is the amount of sand fixation (t·a-1). Msf is the amount of soil wind erosion under the actual surface cover condition (t·ha-1·a-1), and Msfe is the amount of soil wind erosion under the extremely degraded state (t·ha-1·a-1). A is the area of the ecosystem (ha). PSF is the cost of dust pollution reduction, which is 150 yuan·t-1 (DB11/T 659-2018). | ||
Soil fertility maintenance | $\begin{align} & {{V}_{NR}}=\underset{i=1}{\overset{m}{\mathop \sum }}\,(SC+SF) \\ & \times {{C}_{i}}\times {{T}_{i}}\times {{P}_{ci}} \end{align}$ | Ci is the content (%) of soil nitrogen, phosphorus, potassium, and organic matter. Ti is the conversion factors for nitrogen, phosphorus, and potassium to urea, superphosphate, and potassium chloride, which are 2.164, 4.065, and 1.923, respectively (Han et al., 2012). Pci is the market price of urea, superphosphate, potassium chloride, and organic fertilizer, which are 1990, 800, 2200, and 320 yuan·t-1, respectively (DB11/T 659-2018). | ||
Water regulation | $WC=A\times ({{J}_{0}}\times K)\times ({{R}_{0}}-{{R}_{r}})$ ${{V}_{WR}}=WC\times {{P}_{WR}}$ | WC is the precipitation stored by ecosystems (t·a-1). A is the area of the ecosystem (ha). J0 is the annual precipitation (mm). K is the ratio of runoff to the total precipitation. R0 is the runoff yield ratio of the bare land, and Rr is the runoff yield ratio of the ecosystem. PWR is the unit storage cost of the reservoir, which is 6.1107 yuan·m-3 (DB11/T 659-2018). | ||
Water purification | ${{V}_{WP}}=WC\times {{P}_{WP}}$ | PWP is the price of sewage purification, which is 0.95 yuan·t-1. |
Table 2 Identification criteria and calculation methods of the drivers and their impact degree of ecological asset gains and losses |
slopeobs | slopecc | slopeha | Impact degree of climate change (%) | Impact degree of human activities (%) | Explanation |
---|---|---|---|---|---|
> 0 | > 0 | > 0 | $\frac{slop{{e}_{cc}}}{slop{{e}_{obs}}}$ | $\frac{slop{{e}_{ha}}}{slop{{e}_{obs}}}$ | The combined effect of climate change and human activities have led to the gains of an ecological asset |
> 0 | < 0 | 100 | 0 | Climate change has led to the gains of an ecological asset | |
< 0 | > 0 | 0 | 100 | Human activities have led to the gains of an ecological asset | |
< 0 | < 0 | < 0 | $\frac{slop{{e}_{cc}}}{slop{{e}_{obs}}}$ | $\frac{slop{{e}_{ha}}}{slop{{e}_{obs}}}$ | The combined effect of climate change and human activities have led to the losses of an ecological asset |
< 0 | > 0 | 100 | 0 | Climate change has led to the losses of an ecological asset | |
> 0 | < 0 | 0 | 100 | Human activities have led to the losses of an ecological asset |
Table 3 Classification methods of optimization and promotion pathways of ecological assets at the county level in China |
Optimization and promotion type | Composition | Criteria |
---|---|---|
Climate change adaptation | IAb, IIAb, IIIAb, IVAb, ICb, IICb, IIICb | Counties dominated by farmland, forest and grassland ecological assets with balance or gain trend from 1990 to 2018, which were mainly driven by climate change |
Climate change mitigation | IBb, IIBb, IIIBb, IVBb | Counties dominated by farmland and forest ecological assets with loss trend from 1990 to 2018, which were mainly driven by climate change |
Ecological resource restoration | IBa, IIBa, IIIBa, IVBa | Counties dominated by farmland and forest ecological assets with loss trend from 1990 to 2018, which were mainly driven by human activities |
Ecological resource conservation | IAa, IIAa, IIIAa, IVAa | Counties dominated by farmland, forest and grassland ecological assets as the main asset types with gain trend, which were mainly driven by human activities |
Ecological resource management | ICa, IICa, IIICa | Counties dominated by farmland and forest ecological assets with balance trend from 1990 to 2018, which were mainly driven by human activities |
Figure 1 Spatial distribution of ecological asset types at the county level in China |
Figure 2 Interannual variation and range of the ecological asset quality index at the county level in China, 1990-2018 |
Figure 3 Temporal and spatial distribution of ecological flow asset quantities at the county level in China, 1990-2018 |
Figure 4 Temporal and spatial distribution of ecological flow asset value at the county level in China, 1990-2018 |
Figure 5 Spatial and temporal distribution of climate change and human activity driving factors (a-c) and their degree of impact (d-f), which affected the ecological asset gains and losses at the county level in China, 1990-2018 |
Figure 6 Spatial distribution of optimization and promotion pathways of ecological assets at the county level in China |
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