スマート価格設定ツールが電気自動車普及と炭素削減に与える影響：コタキナバル（サバ州）における回帰分析に基づく評価
Influence of Smart Pricing Tool on Electric Vehicle Adoption and Carbon Reduction in Kota Kinabalu, Sabah: Regression-based Assessment (原題)
JAZMINA BAZLA BINTI JUN ISKANDAR, MOHD AZIZUL LADIN, NURUL SHAHADAHTUL AFIZAH ASMAN, Nazaruddin Abdul Taha
🤖 gxceed AI 要約
日本語
マレーシア・コタキナバルでEV充電価格が導入意欲に与える影響を調査。376人へのアンケートと回帰モデルにより、価格と導入確率に強い逆相関（R²=0.9517）を確認。EV移行でCO2排出を73%削減可能と試算し、2025-2030年のシナリオ分析で価格戦略の効果を予測。政策支援ツールも開発。
English
This study examines how EV charging prices affect adoption in Kota Kinabalu, Malaysia. A survey of 376 drivers and regression models show a strong inverse relationship (R²=0.9517). Shifting to EVs could cut CO2 emissions by 73% per 100 km. Scenario forecasts for 2025-2030 suggest aggressive pricing could triple adoption. A Smart EV Pricing Tool was developed to simulate outcomes.
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
日本のEV普及政策（補助金、充電インフラ整備）と比較し、価格感応度の定量分析は参考になる。ただし、日本の文脈ではSSBJや開示との直接的な関連は薄く、運輸部門の脱炭素施策の一環として位置づけられる。
In the global GX context
This paper provides empirical evidence on price elasticity of EV adoption in a Southeast Asian city, contributing to global transport decarbonization literature. It offers a replicable method for pricing scenario analysis that could inform policy in other emerging economies, though its direct relevance to ISSB/TCFD disclosure is limited.
👥 読者別の含意
🔬研究者:EV導入の価格感応度を定量化した回帰モデルとシナリオ分析の手法が参考になる。
🏢実務担当者:充電価格設定やEVシフト戦略の検討に有用なデータを提供する。
🏛政策担当者:運輸部門の脱炭素政策における価格介入の効果を評価するエビデンスとなる。
📄 Abstract（原文）
The decarbonisation of urban transport remains a critical component of Malaysia’s climate response strategy, particularly as electric vehicle (EV) adoption progresses more slowly than regional and global trends. This study investigates how EV charging price interventions can influence potential adoption behaviour in Kota Kinabalu, Sabah, while quantifying the associated carbon dioxide (CO₂) reduction potential. A structured survey of 376 private vehicle owners was conducted to evaluate willingness to transition to EVs under varying charging price conditions. A linear regression model and calibrated logistic function were developed to forecast adoption probability across multiple price scenarios. Results reveal a strong inverse relationship between charging cost and adoption likelihood (R² = 0.9517), indicating that price adjustments remain one of the most impactful levers for accelerating EV uptake. Complementary sustainability analysis shows that shifting from internal combustion engine vehicles to EVs could reduce emissions by 73% for every 100 km travelled. Scenario forecasting from 2025 to 2030 demonstrates that aggressive pricing strategies may triple adoption rates and yield substantial cumulative CO₂ savings. To support policy and planning, a Smart EV Pricing Tool was developed to simulate adoption and emission outcomes across different economic pathways. Findings highlight the importance of pricing reform as an adaptive climate-responsive mechanism and provide a data-driven foundation for transport decarbonisation efforts in Malaysia .
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.17576/jccass.0202.2026.02first seen 2026-08-29 04:32:55
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