包括的政策立案のための人口クラスタリング：ブリティッシュコロンビア州のエネルギー転換における可能性と限界

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🔬研究者:AIクラスタリングの政策応用における方法論的限界と解釈の注意点を学べる。

🏢実務担当者:自治体や地域企業が、地域特性に応じた脱炭素投資やインセンティブ設計を検討する際の参考になる。

🏛政策担当者:地域のエネルギー転換政策において、単一基準ではなく多変量クラスタリングによる包括的ターゲティングの可能性と限界を理解すべき。

Clustering populations across multiple characteristics may enable more holistic policymaking, but policymakers lack guidance for running clustering analysis and interpreting ambiguous results. This thesis develops an artificial intelligence framework and applies it to municipalities in British Columbia to analyse barriers and enablers to energy transitions. It discusses how to evaluate cluster cohesiveness, characterize clusters, and use results to inform policy design. PCA, k-means, and eta-squared are used to cluster and characterize 147 municipalities for targeting residential (heat pump) and transportation (electric vehicle) electrification policies. Ten datasets were cleaned, harmonized, and merged, capturing 14-year trends in residential and transport energy (energy per capita and CO2e per unit energy), 15-year trends in social systems (population, income, housing, education, and labour force participation), geography (remoteness, latitude, and longitude), and institutional capacity for climate action. The residential and transportation scenarios produced two-cluster partitions in which one cluster compounded remoteness, lower education, smaller and stagnant populations, and weaker institutional climate capacity. A new policy could acknowledge the co-occurrence of these barriers within the cluster, and their recurrence across both residential and transportation scenarios. For example, a northern and a southern hub could simultaneously provide training for heat pump and EV charger installation, logistical and financial support for equipment delivery to remote communities, and governance support for local climate planning. Meanwhile, the other, less constrained, cluster could receive lighter instruments such as income-differentiated rebates or regulations. This method can make policy targeting more holistic than existing targeting mechanisms that use only municipal latitude or household income to differentiate policy treatment. Clustering results were sensitive to methodological choices and sometimes ambiguous, and therefore need to be interpreted critically. Clustering quality scores were lower than expected and validation metrics gave conflicting advice on how many cluster partitions to select. Furthermore, the co-occurrence of barriers in a cluster does not tell us why these barriers exist and whether they are the key factors blocking energy transitions. Finally, the analysis excluded the smallest communities for lack of data so that other municipalities could be analysed with more variables. The trade-off between equity and thorough analysis needs to be navigated carefully.

• openalex http://hdl.handle.net/2429/95426first seen 2026-10-01 04:47:03