An influencing mechanism for ecological asset gains and losses and its optimization and promotion pathways in China

doi:10.1007/s11442-022-2027-0

Abstract

Abstract:

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.

Key words: ecological assets, gains and losses, climate change, human activities, optimization and promotion pathways

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.

Figures/Tables 9

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
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
Stock assets	Quality of ecological resources	Quality index	$E{{Q}_{ij}}=\frac{NP{{P}_{ij}}}{MNP{{P}_{i}}}$ $EAQ{{I}_{k}}=\underset{i=1}{\overset{m}{\mathop \sum }}\,\frac{E{{Q}_{ij}}}{{{S}_{ki}}}\times \frac{{{S}_{ki}}}{{{S}_{k}}}$	EQ_ij is the ecological asset quality of the j-th pixel in the i-th ecosystem asset type, and NPP_ij is the NPP (kgC·ha^-1) of it. MNPP_i is the highest NPP nationwide of the i-th type ecological asset (kgC·ha^-1). EAQI_k is the ecological asset quality index of the k-th county. S_ki is the area of the i-th type of ecological asset in the k-th county (ha). S_k 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). P_f is the unit price, and the average selling price of rice, wheat, and corn is 2194 yuan·t^-1.
	Supply services	Raw material supply	${{V}_{PS}}=\underset{j=1}{\overset{m}{\mathop \sum }}\,P{{S}_{j}}\times {{P}_{pj}}$	PS_j is the outputs of the j-th product (t·a^-1), and P_pj 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). M_sc is the soil water erosion amount under the actual surface cover condition (t·ha^-1·a^-1), while M_sce is the soil water erosion amount under the extremely degraded state (t·ha^-1·a^-1). A is the area of the ecosystem (ha); ρ_s is the soil bulk density (t·m^-3) (Han et al., 2016). P_SC 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). M_sf is the amount of soil wind erosion under the actual surface cover condition (t·ha^-1·a^-1), and M_sfe 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). P_SF is the cost of dust pollution reduction, which is 150 yuan·t^-1 (DB11/T 659-2018).
		Soil fertility maintenance	$\begin{align} & {{V}_{NR}}=\underset{i=1}{\overset{m}{\mathop \sum }}\,(SC+SF) \\ & \times {{C}_{i}}\times {{T}_{i}}\times {{P}_{ci}} \end{align}$	C_i is the content (%) of soil nitrogen, phosphorus, potassium, and organic matter. T_i 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). P_ci 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 R_r is the runoff yield ratio of the ecosystem. P_WR is the unit storage cost of the reservoir, which is 6.1107 yuan·m^-3 (DB11/T 659-2018).
		Water purification	${{V}_{WP}}=WC\times {{P}_{WP}}$	P_WP is the price of sewage purification, which is 0.95 yuan·t^-1.

Table 1

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slope_obs	slope_cc	slope_ha	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

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