An ecological network approach to assessing the site suitability of photovoltaic power stations
Data files
Nov 04, 2025 version files 38.11 KB
Abstract
The deployment of photovoltaic power stations in alpine grassland areas of the Tibetan Plateau contributes to the clean energy development and economic growth. Nevertheless, current evaluation frameworks commonly overlook the ecological impacts associated with photovoltaic power stations. To achieve genuinely sustainable development, it is imperative to incorporate ecological factors into the site-selection and planning processes of photovoltaic power stations. In this study, we conducted a comprehensive assessment of the ecological impacts of photovoltaic power stations through the application of an ecological network approach, considering different grassland types and various operational phases. Additionally, we developed an integrated framework that incorporates ecological impact assessments to explore the site location suitability of photovoltaic power stations in the alpine grasslands of the Tibetan Plateau. Our findings indicated that the ecological effects of photovoltaic arrays varied significantly across different grassland types and time of operation. Photovoltaic arrays enhance ecosystem complexity in alpine desert steppe ecosystems. Conversely, they have negative impacts on alpine steppe and alpine meadow ecosystems. The adverse effects are most pronounced during the early years of operation (≤ 4 years). Moreover, we developed a spatial distribution map for evaluating the site location suitability of photovoltaic power stations, which integrates ecological factors. The analysis determined 201,519 km2 of optimal areas for photovoltaic power stations construction, primarily situated in the alpine desert steppe. Notably, when ecological impact factors were omitted from the analysis, there was a respective proportional increase by 155.3% and 193.0% in the areas classified as very suitable and extremely suitable, respectively. This study offers critical insights that can guide the sustainable development of photovoltaic power stations across alpine grassland ecosystems of the Tibetan Plateau. Site-selection policies should mandate ecological assessments referencing developed composite ecological maps for photovoltaic project approvals. Additionally, an ecological certification system should be established to ensure sustainable development. Further research efforts will focus on expanding the evaluation indicators to characterize hydrological progress and the social dimension, specifically considering the local community's support for photovoltaic construction. Ultimately, a more objective and comprehensive assessment of the suitability of photovoltaic power station locations can be ensured.
Dataset DOI: 10.5061/dryad.866t1g24g
General
Authors: Yingxin Wang
Email: wangyingxin14@lzu.edu.cn
Other contributors: Wang, Yingxin; Sun, Jian*; Shang, Hua; Sun, Le; Lan, Xiangyu; Qiezhuo, Lamao; Lu, Jiayue
Organization: State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
Date created: 2025-10-25
Methods of data collection/generation: see article for details
Description of the data and file structure
This dataset has four csv files:
- 1._Effect_size_Grassland_type.csv
- 2._Effect_size_Operational_phase_of_PV_power_stations.csv
- 3._Network_topology_indicatiors_grassland_type.csv
- 4._Network_topology_indicatiors_Operational_phase_of_PV_power_stations.csv
Effect_size_Grassland_type.csv
The data contains effect size estimates (with standard errors, SE) for various ecological indicators across three grassland types under two photovoltaic (PV) array treatments: Gap (between panels) and Under (beneath panels).
Classification:
- Plant characteristics: AGB (Aboveground biomass), Species richness (SR), Plant height (PH), and Coverage (plant coverage);
- Soil physicochemical properties: Soil moisture (SM), Soil temperature (ST), Soil pH, Soil organic carbon (SOC), Soil total nitrogen (TN), Soil ammonium nitrogen (NH₄-N), Soil nitrate nitrogen (NO₃-N), Soil total phosphorus (TP), and Soil available phosphorus (SAP);
- Soil enzyme: Urease (U), Alkaline Phosphatase (ALP), and β-Glucosidase (BG);
- Soil microbial diversity: Shannon and Chao1 indices for Bacteria (B) and Fungi (F).
Grassland Types:
Alpine desert steppe, Alpine steppe, and Alpine meadow
PV Array Treatments:
Gap, Areas between PV panels, Under, Areas directly beneath PV panels. Positive values of effect size estimates indicate an increase, and negative values indicate a decrease compared to the Outside zone (the reference zones located outside the photovoltaic arrays).
Effect_size_Operational_phase_of_PV_power_stations_.csv
The data contains effect size estimates (with standard errors, SE) for various ecological indicators with different years since the operation of photovoltaic panels under two photovoltaic (PV) array treatments: Gap (between panels) and Under (beneath panels).
Classification:
- Plant characteristics: AGB (Aboveground biomass), Species richness (SR), Plant height (PH), and Coverage (plant coverage);
- Soil physicochemical properties: Soil moisture (SM), Soil temperature (ST), Soil pH, Soil organic carbon (SOC), Soil total nitrogen (TN), Soil ammonium nitrogen (NH₄-N), Soil nitrate nitrogen (NO₃-N), Soil total phosphorus (TP), and Soil available phosphorus (SAP);
- Soil enzyme: Urease (U), Alkaline Phosphatase (ALP), and β-Glucosidase (BG);
- Soil microbial diversity: Shannon and Chao1 indices for Bacteria (B) and Fungi (F).
Operational phase of PV power stations: ≤ 4 years and > 4 years;
PV Array Treatments: Gap, Areas between PV panels, Under, Areas directly beneath PV panels. Positive values of effect size estimates indicate an increase, and negative values indicate a decrease compared to the Outside zone (the reference zones located outside the photovoltaic arrays).
Network_topology_indicatiors_grassland_type.csv
The data contains network topology metrics derived from ecological networks across three alpine grassland types under different photovoltaic (PV) array treatments. The metrics characterize the structural properties and node-level roles within each network.
- Grassland Types: Alpine desert steppe, Alpine steppe. and Alpine meadow;
- PV Array Treatments: Under: Areas directly beneath PV panels, Gap: Areas between PV panels, and Outside: Control areas away from PV arrays (natural conditions);
- Network Topology Metrics: Degree: Number of connections per node, Weighted_degree: Sum of connection weights per node (can be positive or negative),
- Eccentricity: Maximum distance from the node to any other node,
- Closeness_centrality: Measure of how close a node is to all other nodes;
- Betweenness_centrality: Measure of how often a node lies on shortest paths between other nodes
- Modularity_class: Community/module assignment for each node,
- Hub: Hub score or importance value (likely a centrality measure),
- Clustering: Local clustering coefficient
- Eigen_centrality: Influence measure based on connections to influential nodes.
Network_topology_indicatiors_Operational_phase_of_PV_power_stations_.csv
The data contains network topology metrics derived from ecological networks for different years since the operation of photovoltaic panels under different photovoltaic (PV) array treatments. The metrics characterize the structural properties and node-level roles within each network.
- Operational phase of PV power stations: ≤ 4 years and > 4 years;
- PV Array Treatments: Under: Areas directly beneath PV panels, Gap: Areas between PV panels, and Outside: Control areas away from PV arrays (natural conditions);
- Network Topology Metrics: Degree: Number of connections per node, Weighted_degree: Sum of connection weights per node (can be positive or negative),
- Eccentricity: Maximum distance from the node to any other node,
- Closeness_centrality: Measure of how close a node is to all other nodes;
- Betweenness_centrality: Measure of how often a node lies on shortest paths between other nodes,
- Modularity_class: Community/module assignment for each node,
- Hub: Hub score or importance value (likely a centrality measure),
- Clustering: Local clustering coefficient,
- Eigen_centrality: Influence measure based on connections to influential nodes.
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