Journal of Sustainable Development and Green Technology

Two allocations can give the same mean reduction in fine particulate matter (PM2.5) and still leave different cities above the reference concentration. We examine this choice with 2021 records for 40 Spanish cities from the World Health Organization’s January 2024 database. The model minimises population-weighted squared excess above 5 µg/m3 subject to a resource budget, local reduction caps and an optional minimum share for each city, and numerical optimisation confirms its analytical solution. At half of assumed total capacity, optimal allocation and proportional allocation both bring the weighted mean concentration down from 9.882 to 8.404 µg/m3; optimal allocation cuts squared excess loss by 9.44% and between-city excess variance by 83.39% relative to proportional allocation. A minimum share of one-quarter of each local reduction cap extends reductions to every city above the reference concentration and raises squared excess loss by 3.01%. A separate feasibility test puts a common concentration ceiling of 10 µg/m3 within reach at 17.12% of assumed total capacity. A ceiling of 9 µg/m3 would push one city past its local reduction cap. These conditional planning results set targets for later assessment of specific interventions.

Decisions must be made both about how food-waste target reductions are allocated and what evidence is used to demonstrate progress. This study develops a bounded policy-accounting framework using the United Nations Environment Programme Food Waste Index 2024. The core sample comprises 38 economies with high-confidence or Eurostat-tagged estimates, representing 82.03 million tonnes of annual household waste. A 50% aggregate reduction is allocated using proportional reductions, equal kilogram reductions, and an explicitly assumed 60% reducibility cap. For the same objective of 41.01 million tonnes, country reduction rates are 50%, 30.43–60%, and 0–60%, respectively. Rankings by per-capita waste and total waste mass share only one economy among their respective top-five lists, and have a Spearman correlation of 0.222. A measured 50% reduction under measurement-error bounds of ±10% corresponds to a true reduction of 38.89–59.09%. Country distinctions also become less clear as error bounds widen. The results support separating rules for allocating physical targets, choosing interventions, and certifying progress. The selected allocation principle should be made public, tested against waste-composition data, and accompanied by comparable measurements before progress toward targets is certified.

Sustainable business transformation is usually described as a technology problem, a leadership problem, or a human-resource problem. In practice, it is all three. This paper develops a cross-country analytical model that links workforce capability, responsible digital leadership, and sustainable operating capacity into a single diagnostic framework. A curated public-indicator extract was prepared from World Bank World Development Indicators, Worldwide Governance Indicators, and labour-market indicator definitions, covering 41 economies with recent values for internet use, R&D intensity, advanced workforce capacity, female senior management, renewable-energy reliance, carbon pressure, governance effectiveness, and digital s kills. The proposed Workforce Sustainable Technology Leadership Index (WSTLI) is used to compare countries, identify capability gaps, cluster transformation profiles, and simulate the effect of combined upskilling, leadership governance, and green technology interventions. The analysis shows that digital readiness alone is insufficient; several economies with high connectivity still show weak sustainability operating capacity, while others with stronger renewable-energy profiles lack the digital-HR capability needed to convert sustainability into scalable business practice. The paper provides a practical measurement architecture for HR leaders, executives, and policy-oriented business analysts who need to align workforce strategy with technology-enabled sustainability.

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.

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.

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.