Employing Bayesian networks for energy transition pathways across Swiss Cities, Midlands, and Alps
スイスの都市、中地、アルプスにおけるエネルギー転換経路のためのベイジアンネットワークの活用 (AI 翻訳)
Jair Campfens, Paul Rebour, Javier Feller, Simon Montfort, Claudia R. Binder
🤖 gxceed AI 要約
日本語
本論文では、スイスのエネルギー戦略2050に向けて、太陽光発電（PV）導入の社会的側面を考慮するため、ベイジアンネットワーク（BN）を用いた地域別の動態モデルを構築。4466人への調査データに基づき、都市・中地・アルプスの3地域で構造学習とパラメータ学習を実施。地域によってPV導入の決定要因が異なることを明らかにし、地域差を考慮した政策介入の必要性を示した。
English
This paper uses Bayesian Networks to model solar PV adoption dynamics across three Swiss regions (cities, Midlands, Alps) using survey data from 4,466 individuals. It identifies region-specific adoption drivers—heat pump ownership in cities, a balanced mix in Midlands, and single-family house ownership in Alps—and tests three policy scenarios. The findings support targeted, regionally differentiated interventions for accelerating solar PV adoption within Switzerland's Energy Strategy 2050.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
スイスの事例ではあるが、日本の自治体レベルでの太陽光発電導入促進策立案に示唆を与える。地域の社会特性を考慮したベイジアンネットワークの活用は、日本のエネルギー転換計画にも応用可能。
In the global GX context
This paper demonstrates a socio-technical approach to energy transition modeling using Bayesian Networks, relevant for regional policy design globally. It shows how survey data can complement traditional techno-economic models to inform differentiated strategies for solar PV adoption.
👥 読者別の含意
🔬研究者:Demonstrates a novel application of Bayesian Networks for integrating social dimensions into energy transition modeling, useful for socio-technical transition research.
🏢実務担当者:Provides a methodology for regional energy planners to incorporate survey data into scenario analysis for solar PV adoption, aiding targeted policy design.
🏛政策担当者:Highlights the need for regionally differentiated policies to accelerate solar PV adoption, based on empirical evidence of varying adoption drivers across regions.
📄 Abstract（原文）
In the context of Switzerland’s ambitious Energy Strategy 2050, solar PV is poised to play a central role. However, traditional energy modelling approaches often overlook the social dimension underlying solar photovoltaics (PV) adoption. This paper demonstrates the potential of using Bayesian Networks (BNs) to integrate the social dimension into energy modelling. BNs can handle uncertainty, incorporate population-level survey data, and simulate dynamic adoption scenarios. Using survey responses from 4466 individuals across Switzerland, we construct data-driven BNs through structure learning and parameter learning across 3 regions, namely the Swiss cities, midlands, and Alps. While the structure of the networks remains consistent, the conditional probabilities reveal region-specific adoption dynamics for solar PV adoption. In cities, heat pump ownership emerges as the dominant factor, while in the Midlands, adoption reflects a balanced mix including electric car ownership and perceived economic viability. In the Alps, single-family house and electric car ownership are most influential. We also tested 3 scenarios. Scenario 1 generated the largest increases, especially in the Alps and midlands. Scenario 2 produced a modest increase, with the strongest relative effect in the Midlands, while scenario 3 was most impactful in cities. We demonstrate that BNs are useful for modelling socio-technical energy transitions. Our findings support the need for targeted, regionally differentiated policy interventions to accelerate solar PV adoption in Switzerland.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.nxener.2026.100750first seen 2026-07-13 04:51:14
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