Keywords
Electric vehicles
competitive landscape
Strategic Resilience
Organizational learning
Scenario foresight
China
How to Cite
Abstract
Purpose: This study develops a Sustainable Competitive Intelligence (SCI) analytical framework that converts public market signals from China’s passenger electric-vehicle sector into structured competitive-landscape intelligence, strategic decision support, and a repeatable monitoring-and-feedback logic.
Methodology/approach: The empirical component is a retrospective, single-vintage analysis of the International Energy Agency Global EV Data 2024 release. The archived historical extract contains no observed post-2023 records used in this analysis; the 2025, 2030, and 2035 STEPS/APS records are retained and analyzed separately as conditional scenario outputs. The analysis combines market penetration, technology shares, HHI, normalized Shannon entropy, demand momentum, charging intensity, first-difference Spearman co-movement diagnostics, scenario sensitivity, and robustness checks.
Originality/Relevance: The study distinguishes public market analytics from organizational CI capability. Sustainable CI is conceptualized as a recurring cycle of planning, sensing, analysis, dissemination, utilization, governance, feedback, organizational learning, and strategic resilience rather than as a synonym for a green market.
Key findings: Observed passenger-EV sales reached 8.10 million in 2023 and represented 38% of new passenger-car sales. The 2023 technology HHI was approximately 0.555 and normalized entropy approximately 0.580. Public charging points reached 2.70 million. STEPS and APS both imply 85% passenger-EV sales penetration in 2035, while technology composition—especially FCEV—remains scenario-sensitive.
Theoretical/methodological contributions: The framework links market, competitive-structure, ecosystem-readiness, and scenario-foresight intelligence to decision-making and a longitudinal feedback loop. Empirical claims are bounded to technology-segment competitive-landscape intelligence; first-difference correlations are exploratory and non-causal.
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