Tomas Baležentis

37062856100

Publications - 6

Decision support for sustainable circular food supply chain in Iran: A fuzzy multi-criteria approach

Tomas Baležentis Dalia Streimikiene Ahmad Bathaei Z. Ardakani L. Agnusdei

Publication Name: Computers and Industrial Engineering

Publication Date: 2025-11-01

Volume: 209

Issue: Unknown

Page Range: Unknown

Description:

As an interconnected network, the food supply chain links multiple actors across production, processing, distribution and consumption. While it plays a vital role in ensuring food security, safety and economic resilience, the sector also faces growing challenges related to its environmental impact and long-term sustainability. Addressing these issues requires a systemic shift toward sustainable circular supply chain models that support net-zero objectives, decarbonization pathways, and ecosystem-wide coordination. This study aims to explore the key factors influencing sustainable circular supply chain management (SCSCM) across five major sectors of the food industry in Iran: grain, dairy, meat, sugar and carbohydrate products. By incorporating the concept of dynamic capabilities into the supply chain context, this study underscores the importance of organizational adaptability and innovation in facilitating the transition toward circular, low-emission supply chains. A snowball-based literature review revealed a lack of prioritization frameworks tailored to the food industry in Iran. To address this gap, the fuzzy Delphi method (FDM) was used to identify critical factors, followed by the fuzzy analytic network process (FANP) to evaluate and rank them based on expert judgment. The findings indicate that supplier facilities, trade credit, supplier risk management, environmental policy and environmental costs are the five most critical enablers of circular and sustainable transformation within the food supply chain. These identified factors offer a foundation for policymakers and industry leaders to design long-term, ecosystem-oriented strategies that enable systemic change and accelerate progress toward net-zero goals within the sector.

Open Access: Yes

DOI: 10.1016/j.cie.2025.111403

MULTI-CRITERIA DECISION ANALYSIS OF CIRCULAR ECONOMY PERFORMANCE IN THE BALTIC STATES: A COMPARATIVE EVALUATION

Tomas Baležentis Dalia Streimikiene Justas Streimikis Ahmad Bathaei

Publication Name: Journal of Business Economics and Management

Publication Date: 2025-10-10

Volume: 26

Issue: 5

Page Range: 1050-1070

Description:

This study embarks on a comparative evaluation of Circular Economy (CE) performance in the Baltic States (Latvia, Lithuania, and Estonia) using a ro-bust multi-criteria decision-making (MCDM) framework. Drawing on 22 key indicators, the research applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to systematically rank the country-level CE implementation across five thematic dimensions: production and consumption, waste management, secondary raw materials, competitiveness and innovation, and global sustainability. The results reveal that Latvia ranks highest, followed by Lithuania and Estonia, underscoring significant differences in waste management efficiency, investment in CE sectors, and material self-sufficiency. The main contribution of this paper lies in the development of a comprehensive, quantitative bench-marking framework that integrates multiple CE indicators and MCDM methods to assess national performance in a data-driven manner. The methodology developed here can serve as a replicable model for CE assessment in other regional or national contexts.

Open Access: Yes

DOI: 10.3846/jbem.2025.24717

Innovative knowledge-based system for forecasting daily hotel operations amid external events using multi-source data: A time-varying parameter state-space model

Tomas Baležentis Dalia Streimikiene Sichao Chen Ji Chen Qinglin Yu Kang Tong

Publication Name: Journal of Innovation and Knowledge

Publication Date: 2026-01-01

Volume: 11

Issue: Unknown

Page Range: Unknown

Description:

Forecasting hotel occupancy during external shocks is particularly challenging due to their disruptive effects. This study develops a forecasting framework that integrates multisource data using a time-varying parameter state-space model (TVP-SSM). In this framework, search engine data (SED) are used to construct exogenous variables, intervention variables are used to reflect the severity of external shocks, and holiday and weekend dummy variables are used to capture the seasonal effect. The empirical study used a dataset from the hospitality industry in Hangzhou, China, covering the period from October 1, 2019, to October 28, 2021, and identified the COVID-19 pandemic as an external shock. The results show that TVP-SSM can effectively simulate the dynamic impact of external events and the periodical effect on hotel occupancy. Additionally, the prediction accuracy of TVP-SSM with intervention variables and periodical variables (TVP-SSM-1) exceeds that of competitive models. Specifically, compared to the naïve model and TVP-SSM without intervention variables and periodic variables (TVP-SSM-2), the prediction accuracy, measured by the root mean square error (RMSE) and mean absolute percentage error (MAPE), increased by 86 % and 87 %, respectively, and by 74 % and 76 %, respectively. These results indicate that the forecasting framework proposed in this study exhibits superior forecasting performance and demonstrates its capability for dynamic impact analysis of hotel occupancy at the industry level under external shocks.

Open Access: Yes

DOI: 10.1016/j.jik.2025.100858

Cloud Model-Based Improved Evidence Theory and Its Applications to Power Systems

Tomas Baležentis Jinming Zhou Dalia Streimikiene Hongqing Zhang Qin Zhou

Publication Name: Romanian Journal of Information Science and Technology

Publication Date: 2026-01-01

Volume: 29

Issue: 1

Page Range: 41-52

Description:

In order to cope with the complex features of ambiguity, randomness and uncertainty in multi-attribute decision-making problems, this paper introduces the DempsterShafer evidence theory in the framework of cloud modeling. First, a cloud model is used to calculate the affiliation of each evaluation metric, which was subsequently converted to a basic confidence assignment function. Second, the game theory idea is borrowed to combine the dynamic and static weights of the evidence in the game, to improve the traditional evidence theory and realize the effective integration of information. The idea of average fit is identified again, and a comprehensive evaluation conclusion is drawn by comparing the closeness of the evaluation object to the optimal and worst solutions. The new electric power system investment project is illustrated, and the applicability of the algorithm is verified.

Open Access: Yes

DOI: 10.59277/ROMJIST.2026.1.04

Can Digital Technology Adoption Drive a “Win–Win” in Corporate Financial Sustainability and Energy Performance? The Role of Chief Digital Officers

Tomas Baležentis Fawaz Baddar ALHussan Zhiyang Shen Lingli Qing Pin Shen

Publication Name: Business Strategy and the Environment

Publication Date: 2026-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This study investigates whether digital technology adoption can drive both sustainable financial performance and enhanced corporate energy efficiency, which has received limited attention in previous research. Grounded in dynamic capability theory and upper echelons theory, and utilizing a two-way fixed-effects regression model, this study analyzes panel data from publicly listed Chinese firms from 2010 to 2022. We assess the “win–win” potential of digital technology adoption on financial and energy outcomes, with a focus on the moderating role of chief digital officers. The results demonstrate that digital technology adoption generates concurrent gains in financial sustainability and energy efficiency, and that the presence of a chief digital officer substantially strengthens these effects. Robustness and endogeneity assessments support these findings. We further identify financing constraints and green technological innovation as mediating channels through which digital technology adoption promotes dual improvements in financial and energy outcomes. Heterogeneity tests indicate stronger effects among larger firms, non-heavy polluting enterprises, and firms located in central regions of China. These findings highlight the strategic value of digital technology in advancing financial sustainability and corporate energy efficiency, promoting broader corporate commitment to digital transformation.

Open Access: Yes

DOI: 10.1002/bse.70671

How can green credit reduce environmental costs? Analysis based on an extended economic growth model with environmental constraints

Tomas Baležentis Chonghui Zhang Dongcai Zhang Xia Fang Shengcheng Zhang

Publication Name: Structural Change and Economic Dynamics

Publication Date: 2026-06-01

Volume: 78

Issue: Unknown

Page Range: 326-342

Description:

Green credit is a financial tool stimulating environmental protection by allocating credit funds for cleaner and energy-efficient activities. However, the mechanisms of credit allocation can affect the effectiveness of green credit. This study discusses the concepts of lending discrimination and endogenous clean technology based on the economic growth model with environmental constraints. This allows explaining the mechanism through which lending discrimination and green innovation influence the effect of green credit on (reduction of) environmental impact. Furthermore, empirical data on Chinese provinces from 2012–-2022 confirm that lending discrimination weakens the positive effect of green credit in reducing environmental impact. Additionally, green credit can reduce environmental impact by enhancing corporate green innovation. This study provides theoretical and empirical evidence for understanding the relationship between green credit and environmental impact and offers insights for optimizing green credit.

Open Access: Yes

DOI: 10.1016/j.strueco.2026.04.001