Search Results (865)

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

The age of entrepreneurship calls for the evolving of learning and development (L&D) models to meet the dynamic demands of innovation, sustainability, and technology innovation. This study examines the trends and issues of L&D models for entrepreneurs, more so focusing on how these models influence business success in a rapidly changing global landscape. The research employs bibliometric analysis, VOSviewer cluster analysis, and co-citation analysis to explore the literature from 1994 to 2024. Data collected from the Web of Science Core Collection database reflect significant trends in entrepreneurial L&D, with particular emphasis on the use of digital tools, sustainability processes, and governance systems. Findings emphasize the imperative role of L&D in fostering entrepreneurship, more so in areas such as digital transformation and the adoption of new technologies. The study also identifies central regions propelling this field, such as UK and USA. Future studies will be centered on the role of digital technologies, innovation, and green business models within entrepreneurial L&D frameworks. This study provides useful insight into the future of L&D within the entrepreneurial domain, guiding academia and companies alike in the planning of effective learning strategies to foster innovation and sustainable business growth. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover, we center the pattern of cost rationalization management regarding the opportunity cost of ecosystem service consumption and propose to test the fundamental hypothesis stating the possible transmission of competition-induced technological innovations to green economic transformation. Our new methodology estimates quantile-specific effects with MM-QR, while identifying the main interaction effects between regulatory pressure and trade competition uses an extended STIRPAT model. The results reveal a paradoxical finding: despite higher environmental policy stringency and opportunity costs of ecosystem services, HT sectors persistently adopt environmentally detrimental cost-reduction approaches. These findings carry important policy implications: (1) environmental regulations for HT sectors require complementary innovation subsidies, (2) trade agreements should incorporate clean technology transfer clauses, and (3) governments must monitor sectoral emission leakage risks. Our dual machine learning–econometric approach provides policymakers with targeted insights for different emission scenarios, highlighting the need for differentiated strategies across clean and polluting HT subsectors. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

This study investigates the mechanisms by which green finance mitigates non-point source pollution. Based on provincial panel data from China spanning 2005 to 2023, this study conducts an empirical analysis that yields several key findings: (1) The development of green finance significantly reduces the intensity of agricultural non-point source pollution. (2) Green finance indirectly contributes to pollution reduction by incentivizing farmers to adopt environmentally sustainable production practices. (3) The pollution control effects of green finance are amplified in regions with advanced digital infrastructure. (4) The impact of green finance on agricultural pollution demonstrates a threshold effect associated with regional innovation capacity—only when innovation capability exceeds a certain threshold does the emission reduction effect of green finance become evident. Theoretically, this study broadens the research dimensions of green finance by integrating farmer behavioral factors and revealing boundary conditions related to technology and innovation. Policy implications include the need to tailor green financial products for agriculture, accelerate the development of rural digital infrastructure, and implement innovation-driven differentiated policies to enhance precision. Full article

Rail transit as a high-energy consumption field urgently requires the adoption of clean energy innovations to reduce energy consumption and accelerate the transition to new energy applications. As an energy-saving fluid machinery, the ejector exhibits significant application potential and academic value within this field. This paper reviewed the recent advances, technical challenges, research hotspots, and future development directions of ejector applications in rail transit, aiming to address gaps in existing reviews. (1) In waste heat recovery, exhaust heat is utilized for propulsion in vehicle ejector refrigeration air conditioning systems, resulting in energy consumption being reduced by 12~17%. (2) In vehicle pneumatic pressure reduction systems, the throttle valve is replaced with an ejector, leading to an output power increase of more than 13% and providing support for zero-emission new energy vehicle applications. (3) In hydrogen supply systems, hydrogen recirculation efficiency exceeding 68.5% is achieved in fuel cells using multi-nozzle ejector technology. (4) Ejector-based active flow control enables precise ± 20 N dynamic pantograph lift adjustment at 300 km/h. However, current research still faces challenges including the tendency toward subcritical mode in fixed geometry ejectors under variable operating conditions, scarcity of application data for global warming potential refrigerants, insufficient stability of hydrogen recycling under wide power output ranges, and thermodynamic irreversibility causing turbulence loss. To address these issues, future efforts should focus on developing dynamic intelligent control technology based on machine learning, designing adjustable nozzles and other structural innovations, optimizing multi-system efficiency through hybrid architectures, and investigating global warming potential refrigerants. These strategies will facilitate the evolution of ejector technology toward greater intelligence and efficiency, thereby supporting the green transformation and energy conservation objectives of rail transit. Full article

Excessive reliance on traditional energy sources such as coal, petroleum, and gas leads to a decrease in natural resources and contributes to global warming. Consequently, the adoption of renewable energy sources in power systems is experiencing swift expansion worldwide, especially in offshore areas. Floating solar photovoltaic (FPV) technology is gaining recognition as an innovative renewable energy option, presenting benefits like minimized land requirements, improved cooling effects, and possible collaborations with hydropower. This study aims to assess the levelized cost of electricity (LCOE) associated with floating solar initiatives in offshore and onshore environments. Furthermore, the LCOE is assessed for initiatives that utilize floating solar PV modules within aquaculture farms, as well as for the integration of various renewable energy sources, including wind, wave, and hydropower. The LCOE for FPV technology exhibits considerable variation, ranging from 28.47 EUR/MWh to 1737 EUR/MWh, depending on the technologies utilized within the farm as well as its geographical setting. The implementation of FPV technology in aquaculture farms revealed a notable increase in the LCOE, ranging from 138.74 EUR/MWh to 2306 EUR/MWh. Implementation involving additional renewable energy sources results in a reduction in the LCOE, ranging from 3.6 EUR/MWh to 315.33 EUR/MWh. The integration of floating photovoltaic (FPV) systems into green hydrogen production represents an emerging direction that is relatively little explored but has high potential in reducing costs. The conversion of this energy into hydrogen involves high final costs, with the LCOH ranging from 1.06 EUR/kg to over 26.79 EUR/kg depending on the complexity of the system. Full article

Although carbon emissions trading systems are universally acknowledged as one of the most potent policy instruments for counteracting hazardous climate trends, and digitalization is seen as a favorable technological means to promote corporate green and low-carbon transformation, few studies have investigated the dark side of both. Using data on Chinese listed companies from 2011 to 2020 and adopting a multi-period DID methodology, this research reveals that, in response to the carbon emissions trading system, firms often adopt low-cost, strategic environmental governance behaviors—namely, carbon washing—to reduce compliance costs and maintain their reputation and image. Furthermore, the study reveals that the information advantages of digital transformation create conditions for the opportunistic manipulation of carbon disclosure. Digitalization amplifies the positive influence of the carbon trading system on corporate carbon washing behavior. Mechanism analysis confirms that the carbon emissions trading system increases the production costs of regulated firms, thereby increasing their carbon washing behavior. Economic consequence analysis confirms that firms engage in carbon washing to gain legitimacy and maintain their reputation and image, which may allow them to obtain opportunistic benefits in the capital market. Finally, this study suggests that the government should adopt supplementary policy tools, such as environmental subsidies, enhanced use of digital technologies to strengthen regulatory capacity, and increased media oversight, to mitigate the unintended consequences of the carbon trading system on corporate behavior. Full article

The energy quota trading policy is a pivotal market-oriented environmental regulation policy that propels the reform of the energy structure. Utilizing panel data from 30 provinces in China covering the period from 2012 to 2022, this study employed a difference-in-differences model to systematically examine the influence of the energy quota trading policy on the decarbonization of energy consumption, and further explores two transmission mechanisms of green technology innovation and energy consumption intensity through mechanism tests. The study reveals several key findings: (1) The energy quota trading policy significantly enhances the decarbonization of energy consumption. (2) This policy encourages the adoption of clean energy by fostering green technological innovation and decreasing overall energy consumption. As a result, it makes a considerable contribution to the decarbonization process in energy usage. (3) The heterogeneity analysis demonstrates that in areas with low levels of industrialization and plentiful resources, as well as within the Yangtze River Economic Belt and the central and western regions, the effects of the policy are significantly more pronounced. Conversely, in regions characterized by high industrialization and limited resources, particularly in the eastern region, the effectiveness of the policy is comparatively diminished. Furthermore, this study not only offers empirical evidence supporting the optimization and enhancement of the energy quota trading policy but also presents recommendations for improving the trading market, regional policies, and fostering green technological innovation. Full article

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

To respond to China’s sustainable development goals, this study uses a dynamic panel data set (2009–2023) and the PSM-DID model to examine how blockchain adoption impacts corporate sustainable development performance (CSDP). The results show that blockchain significantly enhances CSDP by 9.8–12.3%, primarily through two channels (reducing financing constraints by improving transparency and decreasing chairman-CEO duality) to optimize governance. Regional environmental regulation strengthens this relationship. Heterogeneity analysis reveals stronger impacts in unregulated industries, private firms, and central–western regions, while state-owned firms show policy-driven governance improvements. The study enriches the understanding of blockchain’s dual role in balancing efficiency and sustainability, offering insights for integrating digital technology into green policy frameworks. Full article