The transition from a linear economic model to a circular economy (CE) marks a profound change, driving toward better resource management and ecological robustness. Although much previous investigation has concentrated on specific industries, this research takes a broader, multi - sector approach. It delves into how circular economy principles are being incorporated across crucial industries such as textiles, construction, energy, chemical production and agriculture in Uzbekistan. Employing a qualitative research methodology, which involved synthesizing existing literature and scrutinizing policies, the study pinpoints the primary forces driving this change, the obstacles encountered and the connections between different sectors that influence the move toward a circular economy. The results highlight that shared circular practices - like industrial symbiosis, optimizing resource use, adopting renewable energy and developing circular business models - are essential for boosting both environmental sustainability and economic viability. Nevertheless, this transition faces hurdles due to inadequate infrastructure, disjointed governance structures and insufficient skilled personnel, especially within developing nations. Examining Uzbekistan specifically, we observe both growing policy dedication and ongoing structural difficulties, underscoring the need for synchronized governance, investment in green initiatives and robust innovation systems. This work adds to the existing body of knowledge by introducing a conceptual framework that spans multiple sectors. This framework illustrates how industrial systems are interconnected and how these connections contribute to achieving sustainable development goals. Moreover, it offers practical policy suggestions aimed at speeding up circular economy adoptions in Uzbekistan and comparable developing countries.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060201
Vol. 6 Issue. 2 PP. 01–11, (2026)
Realizing the carbon reduction capabilities of deploying renewable energy. is core to the constructive plan of effective climate policy in heterogenous national. contexts. Even though there is an accumulating corpus of panel econometric and machine learning. literature dealing with this relationship, methodological inconsistencies and limited geographic scope leave important empirical questions unanswered. This paper put forward a mixed analytical model combining a within-group Fixed Effects. country-clustered standard errors estimator and a Random Forest ensemble. model to measure the combined effect of renewable energy penetration, economic growth, energy consumption and reliance on fossil fuels per capita carbon. emissions. Findings affirm that the growth of renewable energy has a statistically significant impact. strong and economically significant negative impact on carbon intensity, which remains. following the elimination of country-specific unobserved heterogeneity. Economic structure and energy efficiency are shown to be co-dominant determinants, highlighting. that the energy transition is not decoupled of larger structural. transformation. Articulated income-group and regional heterogeneity issues. single-coefficient policy prescriptions, which propose decarbonization. plans have to be aligned to the national development levels. The machine learning complement validates econometric variable rankings and proves. good cross-country generalizability with country-stratified. cross-validation.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060202
Vol. 6 Issue. 2 PP. 12–33, (2026)
The intersection of sustainable development and green technology has emerged as one of the most intensively studied and consequential domains in contemporary science and engineering, and between 2020 and early 2026, accelerating climate commitments, post-pandemic economic recovery packages, and unprecedented cost reductions across clean energy pathways fundamentally altered the terms of the decarbonisation debate. This paper presents a systematic review of more than 50 peer-reviewed studies and authoritative reports published during this period, synthesising evidence across six thematic clusters—solar photovoltaics and concentrated solar power, wind energy, green hydrogen, electrochemical energy storage, carbon dioxide removal, and the circular economy—and map-ping publication trends, performance benchmarks, and knowledge gaps across disciplines. Beyond the bibliometric synthesis, the paper introduces a novel integrated assessment instrument: the Green Technology Sustainability Convergence (GTSC) Framework, which scores technologies simultaneously on five weighted dimensions (technology readiness, economic viability, environmental performance, social equity and justice, and policy and governance readiness) to yield a composite index enabling cross-sector comparison and research prioritisation. Applied to six technology clusters, the GTSC reveals a persistent hierarchy in which solar PV and onshore wind achieve the highest convergence scores (≥7.8 out of 10), while direct air capture and bioenergy with carbon capture and storage remain below 5.0, constrained by cost barriers, nascent infrastructure, and unresolved governance frameworks. Three over-arching research challenges emerge from the synthesis: the critical mineral bottleneck that threatens supply chains underpinning virtually every green technology; the widening digital–physical sustainability divide, whereby AI-assisted optimisation tools are advancing faster than the physical infrastructure and institutional capacity required to act on their outputs; and the persistent gap between nationally determined contributions and the technology deployment rates needed to remain within 1.5 °C of warming. The paper concludes with a structured research agenda and decision-support guidance for researchers, funding bodies, and policymakers working in this field.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060203
Vol. 6 Issue. 2 PP. 34–46, (2026)
