In order to reduce emissions from the transport sector, the government of Bangladesh aims to accelerate the pace of electric vehicles (EVs) adoption. However, research on consumer perceptions of EVs in the country is limited. This study examined the factors that encourage or discourage consumers from adopting private EVs. A pre-tested structured questionnaire was distributed across the country employing purposive sampling, and 427 responses were obtained. Descriptive statistics were computed, and binary regressions were performed to understand the relationship between respondents' characteristics, factors, and adoption of EVs. For more than 80% of respondents, EVs are energy efficient, environmentally friendly, reduce the usage of fossil fuels, stylish, and elegant. About 90% of respondents cited several discouraging factors, including a lack of sufficient service stations and mechanics, a limited driving range, a higher purchase price, and a low resale value. Approximately 60% of respondents are inclined to buy an EV as their next car. Although respondents showed favourable attitudes towards the characteristics of EVs, nevertheless, they reported many factors (e.g., costs, awareness, taxes) that deter them from buying EVs. These findings could potentially support pertinent authorities in their efforts to accelerate the adoption of EVs. To be more precise, authorities give precedence to the encouragement of EVs purchase through the provision of incentives such as road tax and registration fee reductions. Furthermore, it is essential that authorities allocate resources towards the development of EV-friendly infrastructure, including charging and service stations, while concurrently augmenting public consciousness and understanding of EVs.

This paper examines how the Environmental, Social, and Governance (ESG) concept can be incorporated into project cost-forecasting models and how this incorporation affects predictive accuracy and risk management in the new market, specifically Kazakhstan. It examines the moderating effect of analytical sophistication and institutional contexts on the relationship between ESG integration and project cost performance. A quantitative research design was employed, and 720 project management and finance professionals in the construction, energy, mining, engineering, and infrastructure industries in Kazakhstan participated in data collection. The measurement reliability and validity were checked with the help of Cronbach's alpha, composite reliability (CR), average variance extracted (AVE), and the Kaiser-Meyer-Olkin (KMO) measure. Structural Equation Modeling (PLS-SEM) and the predictive metrics (R 2, f 2, Q 2) demonstrate that the explanatory power of structural relationships is moderate-to-strong and practically important. Findings show that ESG-incorporated forecasting has a substantial positive impact on the performance of project costs and risk reduction, especially with advanced analytical tools, including machine learning (ML). The ESG- cost performance relationship is partially mediated by cost Forecast Accuracy, whereas analytical sophistication enhances the predictive advantages of ESG integration. Regulatory harmonization and data maturity also contribute to the model's effectiveness. The research is among the first empirical applications to validate ESG- and AI-informed cost forecasting in an emerging-market setting, linking sustainability analytics and project management performance. The results have practical implications for managers, policymakers, and financial decision-makers in Kazakhstan and similar emerging markets, and they are replicable across countries to enable concurrent cross-country comparisons and longitudinal analyses of ESG-driven forecasting behaviors.