Search Results (3,260)

The global energy transition has fundamentally reshaped the conditions under which green trade generates sustainable productivity gains. This study investigates whether renewable energy adoption mediates the relationship between green trade export (GTE) and green total factor productivity (GTFP) across 37 OECD economies over 2003–2023. Employing two-way fixed-effects panel regression, dynamic System-GMM estimation, and Hansen’s panel threshold regression with 500 bootstrap iterations, we identify a nonlinear, inverted-N-shaped relationship between GTE and GTFP. Sequential threshold testing reveals a statistically significant double threshold structure: a first clean energy threshold at approximately 8.72% of total final energy consumption and a second threshold at approximately 24.63%, yielding three distinct productivity regimes. Below the first threshold, green trade suppresses GTFP through pollution displacement and insufficient absorptive capacity; between thresholds, green trade exerts a significant positive productivity effect driven by clean technology diffusion and innovation spillovers; above the second threshold, the positive effect moderates, consistent with diminishing returns to green technology absorption. Heterogeneity analysis reveals that early-stage energy transitioners bear disproportionately larger productivity penalties, while advanced transitioners capture stronger above-threshold gains. These findings underscore that trade liberalization alone is insufficient—sustainable productivity growth requires concurrent and targeted investment in renewable energy infrastructure under the post-Paris Agreement framework. Policy implications are presented as evidence-consistent hypotheses, acknowledging that the observational panel framework precludes definitive causal claims pending corroboration from quasi-experimental designs. Full article

The decarbonization of urban public transport requires robust tools to evaluate the operational feasibility and energy implications of bus electrification. This study presents a physics-based modeling framework for estimating the energy consumption of urban bus operations using real-world telemetry data. GPS measurements collected onboard operating buses are used to reconstruct vehicle speed profiles and driving dynamics. The methodology is applied to a representative urban bus route operating in the city centre of Milan, characterized by dense traffic, closely spaced stops, and a high density of signalized intersections. Two operational improvement scenarios are investigated: traffic signal coordination through a “green wave” strategy and the integration of opportunity flash charging (OC) at selected stops. The results show that reducing traffic-related stops improves commercial speed and decreases energy demand, while OC can support battery operation within the constraints of urban service conditions. The proposed framework provides a transferable decision-support methodology for transit agencies planning the electrification of urban bus services and the deployment of supporting infrastructure. Full article

The growing generation of household hazardous waste (HHW) presents critical environmental and public health challenges worldwide. This study investigates prevailing trends in HHW management and analyzes the socio-economic and demographic determinants that influence public perceptions, attitudes, and behaviors toward HHW recycling practices. A comparative mass flow analysis is also conducted to evaluate the limitations of current HHW management practices in Greece and outline policy implementation plans. Statistical findings indicate that income significantly influences recycling behavior. Individuals with annual incomes between €10,001 and €30,000 are less likely to engage in HHW recycling, whereas those earning over €70,000 demonstrate higher levels of recycling participation. The public recognizes the need for green collection points for appropriate HHW management. However, no statistically significant correlation is found between income levels and perceived importance of these facilities. This outcome is attributed to the high proportion (46.7%) of dichotomous variables in the χ² independence test, exceeding the recommended threshold of 25%, which limits interpretability. Such findings indicate the complex interplay of behavioral and socio-economic variables in HHW recycling. The study highlights the importance of targeted public policies, educational interventions, and infrastructure improvements to increase recycling participation and promote sustainable HHW management in Greece. Full article

Based on green growth theory and the theory of spatial externalities, this study systematically examines the mechanisms through which digital finance influences inclusive green growth, as well as the spatial characteristics and nonlinear patterns of urban green total factor productivity (GTFP). Using a sample of 285 prefecture-level cities in China, we constructed fixed effects and threshold effects models for empirical analysis. The results indicate that digital finance has a significant positive impact on inclusive green growth; there exists a dual threshold effect based on urban green total factor productivity (GTFP) and a significant positive spatial spillover effect between the two. Institutional environment, industrial upgrading, and digital infrastructure moderate these relationships, and heterogeneous differences exist across different regions. By integrating digital finance, inclusive green growth, and green total factor productivity into a unified analytical framework, this study provides empirical insights and policy recommendations for advancing urban sustainable development and regional collaborative governance. Full article

Industrial Control Systems (ICSs) face dual imperatives: protecting critical infrastructure from escalating cybersecurity threats while reducing the environmental impact of AI-powered defense mechanisms. Current deep learning anomaly detection approaches achieve security performance but consumes substantial computational resources, creating an environmental paradox in which AI solutions designed to protect infrastructure contribute to carbon emissions at scale. This competition between cybersecurity effectiveness and sustainability objectives intensifies as regulatory frameworks increasingly mandate both security resilience and environmental accountability. This research presents Green-USAD, a sustainability-first AI framework that inverts traditional design paradigms by integrating energy efficiency as a primary architectural constraint from inception rather than applying compression retrospectively. The proposed approach advances green computing for critical infrastructure through four key contributions: (1) a compressed architecture with validation-guided convergence protocols achieving competitive detection performance with minimal computational overhead; (2) a multi-objective optimization framework using the Analytic Hierarchy Process to systematically balance security and sustainability requirements; (3) a hardware-validated energy measurement methodology addressing reproducibility challenges in green AI literature; and (4) a comprehensive evaluation demonstrating cross-datasets and edge-deployment viability. Validation on ICS benchmarks demonstrates that sustainability-first design achieves substantial energy reduction while maintaining operational detection accuracy, with measured training consumption below 1% of conventional approaches and proportional carbon emission reductions. Comparative analysis against post hoc compression baselines establishes fundamental advantages of design-from-inception over train-then-compress paradigms. Edge device deployment on resource-constrained hardware confirms real-world applicability for distributed industrial environments. Results establish that robust cybersecurity and environmental sustainability represent unified rather than competing objectives when intelligent systems are designed with sustainability as a foundational principle. Full article

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development. Full article

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. Full article

This study investigates the rapid, often uncontrolled urban expansion in Khenchela, a medium-sized city in Algeria’s eastern High Plains, and its profound environmental repercussions amid semi-arid fragility. Drawing on sustainable urban development and resilience frameworks, it dissects pressures such as green space reduction (from 45 ha in 1998 to 33 ha in 2023, dropping per capita from 6.1 m² to 3 m² below WHO standards), water scarcity with 35% leakage losses waste mismanagement, informal settlements on hazardous lands, air/soil pollution, and climate vulnerabilities like heat waves and flooding. Employing a mixed-methods approach documentary analysis of (MPLUUP, LUP and MDP) plans, GIS cartography of spatial evolution (2000–2025), statistical demographics, field observations, and institutional critiques, the research exposes governance gaps: fragmented coordination, weak ecological integration, and resource shortages. It reveals socio-spatial disparities across functional zones, underscoring the need for adaptive, participatory strategies that promote polycentric and compact urban forms, enhanced biodiversity, efficient infrastructure, and inclusive governance to strengthen urban resilience. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

Greening, as a concept, is becoming an essential component of contemporary urban planning worldwide, and universities have begun adopting green policies. While there are numerous studies on climate change, green infrastructure, ecology, and sustainability in planning practice, limited scientific research explores how these concepts are embedded within the educational landscape. This paper aims to develop a methodological framework for mapping the “educational greenscape” by evaluating three levels of higher education in a top-down manner: (01) university, (02) faculty, and (03) subject. The research methodology relies on: an extensive literature review and content analysis; a multi-level case study of the University of Belgrade, focusing on an expert survey based on the European University Association framework; curriculum content evaluation at the Faculty of Architecture, using predefined keywords; and the identification of green interventions and their implementation within the subject “Sustainable Territorial Development,” at the Faculty of Architecture. The specific findings indicate that green activities at the institutional level lack resources, communication, and governance. At the faculty level, there is an apparent need for a more even distribution of green urban planning approaches across different faculty courses. However, subject-level assessment showed the successful implementation of the green urban planning concept into teaching and learning methodologies, with it showing transformative potential and providing a universally applicable methodological framework for mapping the educational greenscape. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

In the context of intensifying climate change, understanding the spatial heterogeneity of urban climate risk is critical to effective climate governance in the United States. This study takes 251 major Metropolitan Statistical Areas (MSAs) in the United States as the analytical unit and establishes a multidimensional urban climate risk assessment framework covering hazard risk, exposure vulnerability, and adaptive capacity. Principal Component Analysis (PCA) is adopted for dimensionality reduction to extract key factors, and K-means clustering is used to identify the spatial differentiation characteristics of climate risk across these MSAs. The results show that climate risk in U.S. MSAs presents significant spatial disparities and can be categorized into four types: high resource and adaptive capacity, high exposure with insufficient adaptive support, complex socio-environmental vulnerability, and low current vulnerability with latent cumulative risk. Based on these findings, this study proposes targeted policy recommendations, including promoting inter-MSA coordination and adaptive capacity spillover, implementing gray–green integrated infrastructure development and enhancing social resilience in the southeastern coastal regions, strengthening equity orientation in climate governance, and advancing proactive governance of cumulative and chronic risks. These conclusions provide a reference for relevant authorities to formulate climate policies. Full article

Despite being fundamental to modern infrastructure, the cement and concrete industry is a major contributor to global carbon emissions, necessitating urgent decarbonisation strategies to mitigate climate change and achieve net-zero targets by 2050. This review explores technological pathways and innovations essential for lowering carbon emissions, including low-carbon materials, energy-efficient processes, carbon capture, utilization and storage (CCUS), and advanced production technologies. It also highlights the importance of supportive policy frameworks, financial incentives, and international collaboration in accelerating the transition to a low-carbon industry. While challenges such as high initial costs, resistance to change, and knowledge gaps persist, these can be addressed through innovation, education, and robust financial mechanisms. Furthermore, circular economy principles, sustainable procurement practices, and continued research and development are emphasized as critical enablers of the industry’s transformation. The paper concludes with recommendations for future actions, highlighting the role of cross-sector cooperation, research funding, and knowledge sharing in achieving a sustainable and decarbonised cement and concrete sector that can “go green” for eco-constructions. Full article