JOURNAL ARTICLE
Renewable Energy as a Driver of ESG Transformation of the Energy Complex and Industrial Clusters in Uzbekistan.
Published In: American Journal of Business & Operations Research, 2026, v. 14, n. 1. P. 12 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Sagatovna Abdurashidova, Marina; Rafi, Tehmina 3 of 3
Abstract
This article examines the role of renewable energy (RES) as a key driver of the ESG transformation of Uzbekistan's energy sector and industrial clusters. Based on data from international organizations and specialized analytical reviews, the electricity sector's high dependence on natural gas (approximately 76% of generation in 2023) heightens energy security and sustainability risks amid declining gas production and rising electricity demand. An integrated framework for ESG energy transition management (ESG KPIs + scenario-based effects model) is proposed as a methodological solution, focusing on industrial cluster chains (textiles, construction materials, chemicals/metallurgy, and agro-industrial processing). An assessment of the economic effects of replacing gas-fired power generation with RES is conducted under a scenario in which target benchmarks are achieved by 2030 (scaling RES to 21–27 GW and increasing the share of RES in the electricity supply). The results show that the introduction of renewable energy sources in combination with energy efficiency at the cluster level can provide a sustainable economic effect through the release of gas (alternative cost of fuel), a reduction in electricity costs and losses, an increase in investment attractiveness, and access to "green" financing in the logic of the national green taxonomy. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:American Journal of Business & Operations Research. 2026/01, Vol. 14, Issue 1, p12
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2026
- ISSN:2770-0216
- DOI:10.54216/AJBOR.140103
- Accession Number:192972665
- Copyright Statement:Copyright of American Journal of Business & Operations Research is the property of American Scientific Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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