Search Results (7,831)

Hydrogen is increasingly recognized as a key vector for sustainable energy transitions, deep decarbonization, and enhanced energy security. This review evaluates major hydrogen types, grey, blue, and green, through a comparative assessment of production pathways, cost structures, technological maturity, and market readiness, with a focus on Sub-Saharan Africa (SSA). Grey hydrogen, while currently dominant due to established fossil-based infrastructure and low costs, is associated with high carbon emissions and climate-related risks. Blue hydrogen offers a transitional pathway via carbon capture and storage but faces constraints in SSA from high capital requirements, limited CCS infrastructure, and methane leakage. Green hydrogen, produced through renewable-powered electrolysis, represents the most sustainable long-term option, aligned with global net-zero goals and SSA’s abundant solar and wind resources, despite higher upfront costs. Synthesizing recent techno-economic, policy, and regional studies, the review highlights that prioritizing green hydrogen deployment supported by enabling policy frameworks, targeted investments, and capacity building is critical for unlocking SSA’s hydrogen potential, promoting low-carbon development, and advancing sustainable energy transitions across the region. Full article

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

The study examines the adoption of drought-tolerant maize (DTM) as a climate adaptation measure among smallholder farmers in northern Ghana, using data from 500 households and probit model analysis to determine key adoption drivers. The findings reveal that only 28% of the sampled farmers have adopted DTM, with maize yield, awareness of DTM, access to extension services, and geographical location being significant influencing factors. Among these, maize yield and awareness of DTM have the strongest association with adoption decisions. Additionally, 29% of smallholder farmers employ early planting as a climate adaptation measure. Seed sourcing patterns show that 66% rely on saved seeds, while 33% obtain seeds from input dealers. Encouragingly, 96% of farmers expressed willingness to adopt improved maize varieties. Despite the relatively low adoption rate, targeted policy interventions, such as strengthening agricultural extension services, promoting climate-smart practices, and ensuring continuous research on DTM varieties, can enhance adoption and improve farmers’ resilience to climate change. These findings provide crucial insights for policymakers and agricultural stakeholders aiming to promote sustainable maize production in northern Ghana. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems. Critical insights are embedded within these large-scale spatiotemporal datasets. Spatiotemporal Data Science provides a conceptual and methodological framework for analyzing such data by integrating spatiotemporal thinking, computational infrastructure, artificial intelligence, and domain knowledge. The field advances methods for data acquisition, harmonization, modeling, visualization, and decision support, enabling applications in natural disaster response, public health, climate adaptation, infrastructure resilience, and geopolitical analysis. By leveraging emerging technologies—including generative Artificial Intelligence (AI), large-scale cloud platforms, Graphics Processing Unit (GPU) acceleration, and digital twin systems—Spatiotemporal Data Science enables scalable, interoperable, and solution-oriented research and innovation. It represents a critical frontier for scientific discovery, engineering advancement, technological innovation, education, and societal benefit. Spatiotemporal Data Science is a transdisciplinary field that studies and models dynamic phenomena across space and time by integrating spatial theory, temporal reasoning, artificial intelligence, and scalable computational infrastructure. It enables the development of adaptive, predictive, and increasingly autonomous systems for understanding and managing complex real-world processes. Full article

Achieving a credible net-zero transition requires reliable corporate environmental information to support effective climate governance. When firms overstate environmental commitments without corresponding improvements in actual performance, regulatory signals become distorted, and decarbonization efforts are weakened. This study examines whether stringent command-and-control environmental regulation enhances the credibility of corporate climate commitments. Using the staggered implementation of China’s Air Pollution Prevention and Control Action Plan as a quasi-natural experiment, we construct a firm-level measure of corporate greenwashing that captures the divergence between environmental discourse and regulatory performance. Based on a multi-period difference-in-differences model, the results indicate that environmental regulation significantly reduces corporate greenwashing, with the probability of inconsistency between environmental claims and actual behavior declining by approximately 25 percent relative to the sample mean. Mechanism analysis shows that this effect operates through increased green technological innovation and heightened public environmental concern, which together strengthen substantive compliance and external monitoring. The moderating analysis shows heterogeneous responses across firms: board independence strengthens the policy’s inhibitory effect, while market share and institutional ownership attenuate it. Overall, the findings suggest that command-and-control regulation improves the credibility of disclosure and reinforces the informational foundations necessary for an effective net-zero transition. Full article

Photovoltaic greenhouses (PVGs) are emerging as a key pathway for integrating renewable energy generation with protected horticulture, particularly in the Mediterranean region where high solar irradiance coincides with increasing pressure on water, land, and energy resources. This study presents a structured narrative review with a qualitative comparative synthesis of 24 peer-reviewed case studies, published from 2014 to 2025, identified through structured searches in Scopus and Web of Science and selected based on predefined relevance and eligibility criteria. Results indicate that crop yield responses to PV coverage are highly crop, season and configuration dependent. Yield stability is most consistently reported at lower coverage levels (approximately 10–20%), while higher coverage ranges (30–50%) show more variable outcomes. Mediterranean PVGs generate between 5 and 55 kWh/m² annually, depending on system configuration and PV coverage, supporting partial to high levels of energy autonomy. Economic analyses, based on a limited number of studies, report payback periods of 7.2–14.4 years and internal rates of return (IRR) of 6–14%, particularly under supportive policy frameworks. This review identifies indicative design thresholds, and crop-specific sensitivities while outlining technological and agronomic knowledge gaps and research priorities for optimizing PVG deployment in high-irradiance Mediterranean regions. Overall, PVGs demonstrate strong potential as climate-adaptive, low-carbon solutions for sustainable protected agriculture, although conclusions are constrained by a limited and methodologically heterogeneous evidence base. Full article

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

Air-to-water heat pumps (ASHPs) are a key technology for residential heating decarbonization; however, their seasonal performance is highly sensitive to outdoor temperature variability. Although thermal energy storage (TES) is widely recognized as a means of improving system efficiency, reported performance gains vary due to differences in climatic datasets, control strategies, and modeling assumptions. This study presents a systematic multi-year assessment of the impact of a water-based TES tank on the seasonal performance of a residential ASHP under measured microclimatic conditions. Hourly simulations were conducted for a single-family house at three locations in eastern Croatia using eight years (2018–2025) of measured meteorological data. Building characteristics, system configuration, and operating strategy were kept identical to isolate the influence of storage volume. TES integration reduced annual electricity consumption by 4.8–9.1%, with a multi-year average reduction of 7.02%, and consistently increased the seasonal coefficient of performance (SCOP) across all analyzed years and locations. The highest relative improvements occurred under less favorable microclimatic conditions, emphasizing the importance of diurnal temperature distribution rather than seasonal averages alone. A parametric analysis identified an optimal storage volume of approximately 1000–1500 L when both energy and economic indicators are considered. The results demonstrate that stable and reproducible seasonal efficiency gains can be achieved through a simple, non-predictive operating strategy under continental climatic variability. Full article

The low-carbon transformation of the manufacturing industry is a key path to balance climate goals and industrial competitiveness. This systematic review critically analyzes 145 studies from 2012 to 2025 to explore the low-carbon transformation. Findings show that low-carbon city pilots reduce manufacturing carbon intensity via fiscal and tech expenditures; industrial internet and additive manufacturing reshape low-carbon production, with digital and green process innovations driving emission reduction. Yet, bottlenecks exist: SMEs face digital adaptation and green financing constraints; excessive digitalization causes energy rebound; high-carbon industries’ deep decarbonization is hindered by unproven large-scale economic feasibility of low-carbon tech, alongside policy-technological disconnection, and green finance structural contradictions. This study proposes core solutions: dynamic policy adjustment mechanisms, multi-dimensional SME support systems, and technology–economy coupling evaluation models. It establishes research coordinates for academia, designs policy tools for decision-makers, and provides a technological framework for industrial deep decarbonization, offering global references for balancing climate goals and manufacturing competitiveness. Full article

As China accelerates toward carbon neutrality, decrypting the causal drivers of renewable energy expansion is paramount for effective policy design. We develop a hybrid analytical framework bridging data-driven K2 structural learning with expert-informed Bayesian Networks to map the intricate interdependencies between policy instruments, resource endowments, and socio-economic variables. This causal mapping reveals a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, particularly in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity and backward diagnostic analyses demonstrate that achieving high-growth milestones requires a synergistic convergence of technological cost reductions and mandatory consumption quotas; conversely, the absence of RPS leads to a 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient energy transition. Ultimately, this framework and the identified causal pathways provide a strategic blueprint for other emerging economies navigating the complex transition from subsidy-dependent to market-resilient renewable energy landscapes under stringent climate constraints. Full article

Water governance faces growing challenges from climate change, pollution, and increasing demand, rendering policy evolution a critical research focus. This study analyzes the evolutionary characteristics of China’s national water resources governance policies from 1988 to 2025 through an integrated quantitative textual analysis. Based on 154 authoritative policy documents, the study employs Latent Dirichlet Allocation topic modeling, semantic network analysis, and a tripartite policy instrument coding scheme (command-and-control, market-based, and public participation instruments). The results reveal three key findings: a significant shift in policy attention from early administrative control toward system-oriented governance emphasizing watershed/ecological protection, conservation, and technology; a persistently imbalanced instrument mix with command-and-control tools remaining dominant, despite gradual diversification after 2000; and a three-stage evolutionary trajectory from administrative framework building (1988–1999), through comprehensive management and diversification (2000–2015), to collaborative innovation and basin/ecology integration (2016–2025). This study contributes a long-term empirical perspective on water policy evolution in an emerging economy, demonstrates an integrated textual-analytic approach, and provides actionable insights for optimizing policy mixes through strengthened incentive compatibility, substantive participation mechanisms, and coherent governance-aligned instrument portfolios. Full article

This study focuses on an integrated conceptual framework for smart municipal waste management that combines Pay-as-you-throw (PAYT) with digital technologies emerging from the Internet of Things (IoT), Artificial Intelligence, and blockchain. In the literature, a key limitation has long been recognised: the fragmented implementation of technological solutions and economic instruments in waste management systems. This model is proposed as a multi-layer architecture that integrates user identification, real-time data collection, predictive optimisation, and automated tariff calculation. The framework is expected to reduce mixed-waste volumes and improve operational efficiency while ensuring traceability and transparency in waste management. The framework also provides a practical basis for implementing circular economy principles and advancing climate and urban sustainability goals by linking user behaviour to cost allocation and data-driven monitoring. The findings highlight that measurable environmental benefits depend on the structural integration of behavioural incentives, real-time monitoring, and transparent data governance. The framework demonstrates how PAYT-based incentives, combined with digital monitoring, can reduce mixed waste volumes and associated emissions. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

Greenhouse gas emissions reductions are urgently necessary to mitigate the effects of climate change. Several protocols and agreements are in place to reduce emissions, but global emissions continue to rise nonetheless. This is in part due to emissions offshoring: the shift of manufacturing from countries with developed economies to countries with developing economies. While many countries have achieved reductions through technological advancements, offshoring remains an issue, demonstrated by a global emissions increase despite developed economies reducing their emissions. Trends of atmospheric emission of nitrogen oxides (NO_X), which can be used as a surrogate for gauging sustainability with respect to fossil fuel combustion, confirm this issue. Canada has a developed economy and purports to have reduced emissions in recent decades. Countries accounting for 90% of the dollar value of manufactured goods imported to Canada from 1990 to 2022 were analyzed. Canada shows a decrease in NO_X emissions attributed to manufacturing alongside an increase in imports of manufactured products. Human Development Index (HDI), a United Nations metric for the development level of a country, was plotted against relative manufacturing NO_X emissions on a country basis. There are distinct trends over the time period among low, medium, high, and very high HDI categories for the top countries importing to Canada. Piecewise linear regressions were run for each country, allowing the number of breaks to be equivalent to the number of HDI category changes spanned over the time period. As the HDI category increased, the number of countries with an inverse relationship between HDI and NO_X emissions grew. Countries with very high HDI almost all showed that the HDI increase corresponded to lower emissions of NO_X, while countries with lower HDI categories showed a reduction in this trend. The results support the theory that Canada has offshored manufacturing emissions rather than decreasing the emissions they are responsible for in terms of their manufactured goods. Full article