Large-scale discourse analysis reveals least-regret integration strategies for variable renewable energy

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Demonstrates a novel integration of AI social sensing with energy system modeling to incorporate societal preferences into VRE deployment planning.

🏢実務担当者:Provides a methodology to assess social acceptance and grid stability, helping energy planners design deployment strategies that minimize public opposition and enhance system security.

🏛政策担当者:Offers evidence that cost-optimal VRE deployment may be suboptimal when societal constraints are considered, supporting policies that promote distributed renewable energy and public engagement.

<title>Abstract</title> <p>The pursuit of carbon neutrality requires a fundamental restructuring of energy systems. While diverse technological pathways exist, the plummeting costs of wind and solar have positioned variable renewable energy (VRE) as a central pillar for deep decarbonisation. However, VRE expansion has so far been driven primarily by techno-economic cost minimization, often overlooking the complex societal constraints that dictate real-world feasibility. Here we undertake a sociotechnical analysis to identify least-regret VRE pathways, integrating AI-enabled social sensing with high-resolution energy system modelling. We decode, validate and integrate latent preference structures from large-scale public discourse to construct realistic transition scenarios, using China as a representative case study for rapid decarbonisation. The integration of socially sensed data generates markedly different transition topologies compared with cost-optimal baselines: rather than concentrating deployment in resource-rich western regions, least-regret strategies necessitate a spatially distributed layout to enhance equity and system security. We identify a robust operational window of 70–75% VRE penetration that effectively balances ambitious climate targets with grid stability constraints. While quantifying societal values adds computational complexity, it provides a transparent mechanism to resolve socio-technical frictions. This work demonstrates how formalizing social sensing within engineering frameworks can challenge conventional planning assumptions, offering a scalable methodology transferable to other economies navigating the universal trade-offs of the net-zero transition.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-9920206/v1first seen 2026-07-10 04:40:28