Electric Vehicles’ Contribution to Sustainable Long-Term Investment for Public and Private Sectors: The Role of Value Co-Creation
Bibliographic record
Abstract
Purpose: This study aims to explore the viability of major economic benefits from timely investment and development of electric vehicles (EVs) across public and private sectors. It also examines how value co-creation can be seen as a strategy to overcome high initial capital costs, and discusses implications for adoption, profitability, and sustainability. Design/Methodology/Approach: This study adopts a systematic literature review (SLR) approach to synthesise information from 37 articles published between 2003 and 2024 across the fields of business management, public policy, and sustainable development. This analysis is supplemented by descriptive data established from institutional reports (IEA, WHO, etc.) with the statistical records of leading EV manufacturers. Findings: First, the paper elucidates how EV-targeted initiatives can realise robust returns on investment (ROI) through revenue stream diversification, market valuation increases, and conducive policy incentives. Second, it highlights how much the public sector could save in healthcare from cutbacks in vehicle emissions, as well as budgetary advantages from climate-friendly policies. Third, it shows how models of value co-creation, for instance shared R&D labs, battery leasing, and government-industry partnerships, can help overcome the high upfront costs of EV production and acquisition. Taken together, these findings demonstrate a synergy that there is a tight linkage between environmental gains and the bottom line. Originality: Related works have predominantly aimed to assess EV’s environmental value proposition; this paper examines financial and strategic constituents. An analysis of how co-creation as a phenomenon could subdue the financial barrier is also presented. The study presents a wide-reaching argument of how EV development has influenced the public and private sectors, offering new perspectives and approaches to sustainable business. Keywords: Electric Vehicle (EVs); Value Co-Creation; Sustainable Investment; Public Sector Profitability; Collaborative Innovation, Capital Costs. Citation: Sujatmiko, S. A., Halim, R. E., Sumiyarto and Apriyana, N. (2025): Electric Vehicles’ (EVs) Contribution to Sustainable Long-Term Investment for Public and Private Sectors: The Role of Value Co-Creation. World Journal of Entrepreneurship, Management and Sustainable Development, Vol. 21, No. 2, pp. 133-148.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".