大规模光伏开发对高寒荒漠化草原生态系统碳储量的影响

doi:10.17521/cjpe.2024.0465

植物生态学报 ›› 2025, Vol. 49 ›› Issue (11): 1791-1804.DOI: 10.17521/cjpe.2024.0465 cstr: 32100.14.cjpe.2024.0465

大规模光伏开发对高寒荒漠化草原生态系统碳储量的影响

刘强¹^,², 马鸿元²^,^*(), 彭云峰³, 拉本¹, 叶得力², 张嘉宸², 赖俊华²

¹青海师范大学生命科学学院, 西宁 810008
²青海黄河上游水电开发有限责任公司高原能源产业与生态研究中心, 西宁 810008
³中国科学院植物研究所饲草种质高效设计与利用全国重点实验室/植被与环境变化重点实验室, 北京 100093

收稿日期:2024-12-20 接受日期:2025-05-01 出版日期:2025-11-20 发布日期:2025-11-20
通讯作者: *马鸿元(ma_hongyuan@foxmail.com)
基金资助:
青海省重大科技专项(2021-SF-A7-2)

Influence of large-scale photovoltaic development on carbon storage in an alpine desert grassland ecosystem

LIU Qiang¹^,², MA Hong-Yuan²^,^*(), PENG Yun-Feng³, LA Ben¹, YE De-Li², ZHANG Jia-Chen², LAI Jun-Hua²

¹College of Life Sciences, Qinghai Normal University, Xining 810008, China
²Research Center for Plateau Energy Industry and Ecology, Qinghai Huanghe Hydropower Development Co Ltd, Xining 810008, China
³State Key Laboratory of Forage Breeding-by-Design and Utilization (SKL-FBDU), Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Received:2024-12-20 Accepted:2025-05-01 Online:2025-11-20 Published:2025-11-20
Supported by:
Major Science and Technology Special Project of Qinghai Province(2021-SF-A7-2)

摘要/Abstract

摘要： 草地生态系统中储存着大量有机碳。近年来, 草地分布区大规模光伏电站建设极大改变了园区内微气候、植被和土壤特征, 进而影响生态系统碳循环。然而, 光伏开发对植被和土壤储量的影响还缺乏系统研究。为探究其对荒漠化草原生态系统碳储量的影响, 该研究采用以空间代替时间的方法, 分析共和县塔拉滩光伏电站内的植被地上生物量碳密度、土壤总碳、有机碳和易氧化有机碳储量等指标随建成年限的变化规律。结果表明: (1)研究区域内土壤总碳在光伏板下、板间和站外的平均储量分别为: 118.83、119.08、108.15 t·hm^-2; 有机碳储量分别为61.97、61.29、58.14 t·hm^-2; 易氧化有机碳储量分别为23.95、25.21、19.18 t·hm^-2; 植被地上生物量碳密度分别为47.58、43.69、26.03 g·m^-2; 除有机碳和板下易氧化有机碳储量外, 板下和板间均显著大于站外。(2)随电站建成年限的增加, 植被地上生物量碳密度在板下和板间分别以6.91和10.01 g·m^-2·a^-1的速率增长。土壤有机碳和易氧化有机碳储量与光伏建成年限间呈显著的正相关关系。(3)植被地上生物量碳密度主要受光伏建设和植被盖度的影响, 易氧化有机碳储量同样受光伏的影响最大。总之, 光伏建设尽管在短期内对土壤总碳储量的影响统计上尚不显著, 但是会显著增加植被地上生物量碳密度、土壤有机碳和易氧化有机碳储量。将来随着光伏建设年限的延长, 该区域土壤将持续发挥碳汇功能。因此, 大规模光伏开发对提升我国高寒荒漠化草地固碳能力、实现碳中和目标有积极作用。

关键词: 光伏生态效应, 土壤固碳, 荒漠化草原, 草地碳储量, 碳汇

Abstract:

Aims Grassland ecosystems store large amounts of organic carbon. In recent years, the construction of large-scale photovoltaic (PV) power plants in grassland areas has dramatically altered the microclimate, vegetation, and soil characteristics of the grassland ecosystem, thereby affecting the carbon cycle. However, there is a lack of systematic research regarding the effects of PV development on vegetation and soil storage.

Methods To investigate the impact on the carbon stock of desertified grassland ecosystems, this study adopts a space-for-time substitution method to analyze the changing patterns of aboveground biomass carbon density, total soil carbon, organic carbon, readily oxidizable organic carbon stock, and other indices at the Tala Beach Photovoltaic Power Station in Republican County, Qinghai Province, considering different years of construction.

Important findings The results showed that: (1) under the PV panels, between the panels, and outside the station in the study area, the average total soil carbon storage were 118.83, 119.08, and 108.15 t·hm^-2, respectively; the organic carbon was 61.97, 61.29, and 58.14 t·hm^-2, respectively; the readily oxidizable organic carbon was 23.95, 25.21, 19.18 t·hm^-2; the aboveground biomass carbon density was 47.58, 43.69, 26.03 g·m^-2, respectively. Except for the organic carbon and readily oxidizable organic carbon storage under the panels, values of other indices under and between panels were significantly larger than those outside the station. (2) As the establishment time of the power station increased, the aboveground biomass carbon density of the vegetation increased at a rate of 6.91 and 10.01 m^-2·a^-1 under the panels and between the panels, respectively. There was a significant positive correlation between soil organic carbon and easily oxidized organic carbon stocks and the number of the establishment year of PV. (3) Above-ground biomass carbon density was mainly affected by the construction of PV and vegetation cover, and the construction of PV also affected the easily oxidized organic carbon stock the most. In conclusion, although the construction of PV did not significantly affect the total soil carbon storage in the short term, it significantly increased the aboveground biomass carbon density soil organic carbon and readily oxidizable organic carbon stock. In the future, the soil in the region will continue to function as a carbon sink as the establishment time of PV increases. Therefore, large-scale photovoltaic development has a positive effect on enhancing the carbon sequestration capacity of alpine desert grassland in China and achieving the goal of carbon neutrality.

Key words: photovoltaic ecological effects, soil carbon sequestration, desertified grasslands, grassland carbon stocks, carbon sinks

刘强, 马鸿元, 彭云峰, 拉本, 叶得力, 张嘉宸, 赖俊华. 大规模光伏开发对高寒荒漠化草原生态系统碳储量的影响. 植物生态学报, 2025, 49(11): 1791-1804. DOI: 10.17521/cjpe.2024.0465

LIU Qiang, MA Hong-Yuan, PENG Yun-Feng, LA Ben, YE De-Li, ZHANG Jia-Chen, LAI Jun-Hua. Influence of large-scale photovoltaic development on carbon storage in an alpine desert grassland ecosystem. Chinese Journal of Plant Ecology, 2025, 49(11): 1791-1804. DOI: 10.17521/cjpe.2024.0465

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2024.0465

https://www.plant-ecology.com/CN/Y2025/V49/I11/1791

图/表 15

图1 青海塔拉滩不同建成年限光伏电站研究区域概况。

Fig. 1 Overview of the study area for photovoltaic power plants of different construction years in Talatan.

表1 不同混合线性模型的似然比检验结果

Table 1 Likelihood ratio test results for different mixed linear models

模型 Model	参数数量 Number of parameters	AIC	BIC	对数似然 Log-likelihood	偏差 Deviation	χ²	自由度 df	p
model_null	4	1 231.62	1 243.85	-611.81	1 223.62	NA	NA	NA
model_board	5	1 233.59	1 248.87	-611.79	1 223.59	0.03	1	0.860
model_depth	6	1 209.19	1 227.53	-598.59	1 197.19	26.40	1	<0.001
model_all	7	1 211.16	1 232.56	-598.58	1 197.16	0.03	1	0.871

图2 光伏板下、板间和站外植被地上生物量碳密度的单因素方差分析。不同小写字母代表观测指标在不同采样位置之间差异显著(p < 0.05)。

Fig. 2 One-way ANOVA of aboveground biomass carbon density under the photovoltaic panels, between the panels, and outside the station. Different lowercase letters represent significant differences between sampling locations (p < 0.05).

图3 不同光伏电站建成年限板下、板间和站外植被地上生物量碳密度的比较(A), 以及植被地上生物量碳密度净增长量(平均值±标准误)随光伏电站建成年限的变化(B)。

Fig. 3 Difference of aboveground biomass carbon density under the photovoltaic panels, between the panels, and outside the station (A), and changes in the net aboveground biomass carbon density (mean ± SE) under the photovoltaic panels and between the panels after photovoltaic power station construction (B).

图4 光伏板下、板间和站外土壤碳储量的单因素方差分析。EOC, 易氧化有机碳; SOC, 有机碳; TC, 总碳。不同小写字母代表观测指标在不同采样位置之间差异显著(p < 0.05)。

Fig. 4 One-way ANOVA of soil carbon stocks under the photovoltaic panels, between the panels, and outside the station. EOC, readily oxidizable organic carbon; SOC, organic carbon; TC, total carbon. Different lowercase letters represent significant differences between sampling locations (p < 0.05).

图5 不同建成年限光伏电站板下、板间和站外有机碳储量的比较。

Fig. 5 Comparison of organic carbon stocks under the photovoltaic panels, between the panels, and outside the photovoltaic power station with different years of construction.

表2 土壤有机碳储量(SOC)的最优混合线性模型参数估计

Table 2 Estimates of optimal mixed linear model parameters for soil organic carbon stock (SOC)

固定因子 Fixed factor	系数估计 Coefficient estimation	标准差 Standard deviation	自由度 df	t	p
截距 Intercept	0.539	3.198	49.697	0.169	0.867
光伏建成年数 Years after construction (a)	1.081	0.382	50.799	2.832	0.007^**
10-20 cm深度 Depth 10-20 cm	0.466	2.674	48.612	0.174	0.862
20-40 cm深度 Depth 20-40 cm	-12.959	2.683	49.168	-4.830	<0.001^***

图6 不同建成年限光伏电站板下、板间和站外易氧化有机碳储量的比较。

Fig. 6 Comparison of easily oxidizable organic carbon stocks under the photovoltaic panels, between the panels, and outside the photovoltaic power station with different years of construction.

表3 易氧化有机碳储量(EOC)的最优混合线性模型参数估计

Table 3 Estimates of optimal mixed linear model parameters for readily oxidizable organic carbon stock (EOC)

固定因子 Fixed factor	系数估计 Coefficient estimation	标准差 Standard deviation	自由度 df	t	p
截距 Intercept	-8.915	3.907	45.12	-2.282	0.027^*
光伏建成年数 Years after construction (a)	2.384	0.476	48.88	5.011	<0.001^***
10-20 cm深度 Depth 10-20 cm	1.582	3.357	45.13	0.471	0.640
20-40 cm深度 Depth 20-40 cm	-4.163	3.323	45.85	-1.253	0.217

图7 不同建成年限光伏电站板下、板间和站外总碳储量的比较。

Fig. 7 Comparison of total carbon stocks under the photovoltaic panels, between the panels, and outside the photovoltaic power station with different years of construction.

表4 总碳储量(TC)的最优混合线性模型参数估计

Table 4 Estimates of optimal mixed linear model parameters for total carbon stock (TC)

固定因子 Fixed factor	系数估计 Coefficient estimation	标准差 Standard deviation	自由度 df	t	p
截距 Intercept	7.904	7.964	44.781	0.992	0.326
光伏建成年数 Years after construction (a)	0.922	0.938	45.854	0.984	0.331
10-20 cm深度 Depth 10-20 cm	0.349	5.618	42.610	0.062	0.951
20-40 cm深度 Depth 20-40 cm	7.747	5.697	44.849	1.360	0.181

图8 植被地上生物量碳密度驱动因子的结构方程模型。黑色实线代表显著的正向影响; 黑色虚线代表显著的负向影响; 灰色实线代表不显著的正向影响; 灰色虚线代表不显著的负向影响; *, p < 0.05; **, p < 0.01; ***, p < 0.001。AGBD, 地上生物量碳密度; C, Margalef丰富度指数; D, Simpson优势度指数; E, Pielou均匀度指数; H′, Shannon-Wiener多样性指数; PV, 光伏电站建成时间; VEGOVC, 植被盖度。

Fig. 8 Structural equation model of drivers of aboveground biomass carbon density. Solid black lines represent significant positive impacts; dashed black lines represent significant negative impacts; solid gray lines represent non-significant positive impacts; and dashed gray lines represent non-significant negative impacts. *, p < 0.05; **, p < 0.01; ***, p < 0.001. AGBD, above ground biomass carbon density; C, Margalef richness index; D, Simpson dominance index; E, Pielou evenness index; H′, Shannon-Wiener diversity index; PV, photovoltaic power plant construction time; VEGOVC, vegetation coverage.

图9 结构模型方程中各因子对植被地上生物量碳密度的总效应值。AGBD, 地上生物量碳密度; C, Margalef丰富度指数; D, Simpson优势度指数; E, Pielou均匀度指数; H′, Shannon-Wiener多样性指数; PV, 光伏电站建成时间; VEGOVC, 植被盖度。

Fig. 9 Total effect values of the factors on the aboveground biomass carbon density in the structural equation model. AGBD, above ground biomass carbon density; C, Margalef richness index; D, Simpson dominance index; E, Pielou evenness index; H′, Shannon-Wiener diversity index; PV, photovoltaic power plant construction time; VEGOVC, vegetation coverage.

图10 土壤易氧化有机碳储量(EOC)驱动因子的结构方程模型。AGBD, 地上生物量碳密度; BD, 土壤容重; DUL, 田间持水量; PV, 电站建成时间; Sand, 土壤含砂量; STK, 土壤全钾含量; STN, 土壤全氮含量; STP, 土壤全磷含量。黑色实线代表显著的正向影响; 黑色虚线代表显著的负向影响; 灰色实线代表不显著的正向影响; *, p < 0.05; **, p < 0.01; ***, p < 0.001。

Fig. 10 Structural equation model of drivers of easily oxidizable organic carbon stock (EOC) in soils. AGBD, above ground biomass carbon density; BD, soil bulk density; DUL, field capacity; PV, power plant construction year; Sand, soil sand content; STK, soil total potassium content; STN, soil total nitrogen content; STP, soil total phosphorus content. Solid black lines represent significant positive impacts; dashed black lines represent significant negative impacts; solid gray lines represent non-significant positive impacts. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

图11 结构模型方程中各因子对土壤易氧化有机碳储量(EOC)造成的总效应值。AGBD, 地上生物量碳密度; BD, 土壤容重; DUL, 田间持水量; PV, 电站建成时间; Sand, 土壤含砂量; STK, 土壤全钾含量; STN, 土壤全氮含量; STP, 土壤全磷含量。

Fig. 11 Total effect values of the factors on readily oxidizable organic carbon stock (EOC) in soils in the structural equation model. AGBD, above ground biomass carbon density; BD, soil bulk density; DUL, field capacity; PV, power plant construction year; Sand, soil sand content; STK, soil total potassium content; STN, soil total nitrogen content; STP, soil total phosphorus content.

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大规模光伏开发对高寒荒漠化草原生态系统碳储量的影响

Influence of large-scale photovoltaic development on carbon storage in an alpine desert grassland ecosystem

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