DOI: 10.5061/dryad.866t1g24g
title: An ecological network approach to assessing the site suitability of photovoltaic power stations

description: 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.
description: # An ecological network approach to assessing the site suitability of
 photovoltaic power stations Dataset DOI:
 [10.5061/dryad.866t1g24g](https://doi.org/10.5061/dryad.866t1g24g) ##
 General Authors: Yingxin Wang Email:
 [wangyingxin14@lzu.edu.cn](mailto: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.
 1._Effect_size_Grassland_type.csv 2.
 2._Effect_size_Operational_phase_of_PV_power_stations.csv 3.
 3._Network_topology_indicatiors_grassland_type.csv 4.
 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 &gt; 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 &gt; 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. ## Access information NA