Work

Identifying the causal effects of photovoltaic installations on grassland productivity using double machine learning: a case study in inner Mongolia

Year: 2026

Type: article

Abstract: Driven by the global energy transition and the “dual-carbon” goals, the rapid deployment of large-scale photovoltaic (PV) installations has profoundly reshaped land surface processes. This transformation is particularly pronounced in arid and semi-arid grassland ecosystems, where the potential ecological impacts of PV construction remain both critical and controversial. However, most existing studies rely primarily on correlation analyses, which fail to accurately identify the true causal effects of PV installations on ecosystem productivity. In this study, we focus on typical grasslands in Inner Mongolia, China, integrating multi-source remote sensing datasets including MODIS net primary productivity (NPP), meteorological, topographic, and anthropogenic factors. A double machine learning (DML) approach is employed within a quasi-experimental framework to quantify the ecological causal effects of PV construction. The results reveal that the average treatment effect (ATE) of PV installation on grassland NPP is − 0.00427 (p > 0.05), indicating a statistically insignificant overall impact. Nevertheless, substantial spatial heterogeneity exists: approximately 62.1% of PV sites exhibit positive ecological effects, whereas 37.9% show negative ones. SHapley Additive exPlanations (SHAP) based analysis further identifies that the spatial variability of PV-induced ecological effects is primarily regulated by environmental factors such as distance to water bodies, mean annual temperature, potential evapotranspiration, soil moisture, and drought index. This study demonstrates the effectiveness and advantages of the DML framework in identifying causal ecological interventions. The findings provide a scientific basis for implementing “ecology-prioritized, site-specific” PV development strategies and highlight the necessity of integrating ecological baseline assessments and spatially precise management to achieve a synergy between energy security and ecosystem conservation.

Source: Scientific Reports

DOI: 10.1038/s41598-026-39023-3

Authors: Zhangqi Yu, Zuopei Zhang, Runsong Yuan

Institutions: North China Electric Power University, Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System

Language: English

Topic: Photovoltaic Systems and Sustainability

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Life on land, Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,690