Search Results (504)

The exponential growth of the digital economy has transformed data centers into major energy consumers, yet their inflexible power consumption patterns and substantial waste heat generation pose significant challenges to grid stability and carbon neutrality targets. Existing energy management strategies often overlook the deep coupling potential between computing workload flexibility, thermal dynamics, and carbon trading mechanisms, leading to suboptimal resource utilization. To address these issues, this study proposes a collaborative low-carbon economic scheduling strategy for data center microgrids. A multiple-dimensional coupling framework is established, integrating a queuing theory-based model for delay-tolerant workload shifting and a heat-determined computing mechanism for active waste heat recovery (WHR). Furthermore, a mixed-integer linear programming (MILP) model is formulated, incorporating a linearized tiered carbon trading mechanism to facilitate source–load coordination. Simulation results demonstrate that the proposed strategy achieves a dual optimization of economic and environmental benefits, reducing total operating costs by 11.7% while minimizing carbon emissions to 6879 kg compared to baseline scenarios. Additionally, by leveraging temperature aware load migration, the daily weighted power usage effectiveness (PUE) is optimized to 1.2607. These findings quantify the marginal benefits of load flexibility under tiered pricing, providing insights for operators to balance service timeliness and energy efficiency in next generation green computing infrastructure. Full article

The institutional environment constitutes the external foundation for corporate development. In the process of China’s modernization, addressing the fiscal constraints on corporate green development is a key issue in advancing the green transformation of the economy, as well as a new approach to understanding the implementation gaps in environmental regulations and the challenges facing the development of green finance. This paper draws on new institutional economics theory to construct an analytical framework of “institutional incentives-behavioural choices-performance outcomes.” Using unbalanced panel data from 2008 to 2022 on listed companies in the Shanghai and Shenzhen A-share markets and prefecture-level cities, a two-way fixed effects model is employed to systematically examine the impact of fiscal vertical imbalances on the efficiency of corporate green development. Heterogeneity analysis reveals the ‘institutional sensitivity gradient’ phenomenon, with the inhibitory effects of fiscal vertical imbalances being particularly pronounced among institutionally sensitive groups such as labour and capital-intensive enterprises, heavily polluting enterprises, mature and declining stage enterprises, and eastern coastal enterprises. Fiscal vertical imbalances severely constrain the pace of green transformation in traditional enterprises and the growth of green industries. It is necessary to reconfigure the central-local fiscal relationship oriented toward green development, innovate ecological compensation and green debt coordination mechanisms, and establish an incentive-compatible institutional environment to resolve the “green paradox.” Full article

The accelerating global demand for sustainable energy, driven by population growth, industrialization, and environmental concerns, has intensified the search for renewable alternatives to fossil fuels. Biofuels, including bioethanol, biodiesel, biogas, and biohydrogen, offer a viable and practical pathway to reducing net carbon dioxide (CO₂) emissions. Yet, their large-scale production remains constrained by biomass recalcitrance, high pretreatment costs, and the enzyme-intensive nature of conversion processes. Recent advances in enzyme immobilization using magnetic nanoparticles (MNPs), covalent organic frameworks, metal–organic frameworks, and biochar have significantly improved enzyme stability, recyclability, and catalytic efficiency. Complementary strategies such as cross-linked enzyme aggregates, carrier-free immobilization, and site-specific attachment further reduce enzyme leaching and operational costs, particularly in lipase-mediated biodiesel synthesis. In addition to biocatalysis, nanozymes—nanomaterials exhibiting enzyme-like activity—are emerging as robust co-catalysts for biomass degradation and upgrading, although challenges in selectivity and environmental safety persist. Green synthesis approaches employing plant extracts, microbes, and agro-industrial wastes are increasingly adopted to produce eco-friendly nanomaterials and bio-derived supports aligned with circular economy principles. These functionalized materials have demonstrated promising performance in esterification, transesterification, and catalytic routes for biohydrogen generation. Technoeconomic and lifecycle assessments emphasize the need to balance catalyst complexity with environmental and economic sustainability. Multifunctional catalysts, process intensification strategies, and engineered thermostable enzymes are improving productivity. Looking forward, pilot-scale validation of green-synthesized nano- and biomaterials, coupled with appropriate regulatory frameworks, will be critical for real-world deployment. Full article

The shift towards the integration of and transition to renewable energy has led to an increase in renewable energy communities (RECs) and smart grids (SGs). The significance of these RECs is mainly energy self-sufficiency, energy independence, and energy autonomy. Despite this, in low- and middle-income countries and regions like Pakistan and the Middle East, SGs and RECs are still in their initial stage. However, they have potential for green energy solutions rooted in their unique geographic and climatic conditions. SGs offer energy monitoring, communication infrastructure, and automation features to help these communities build flexible and efficient energy systems. This work provides an overview of Pakistani and Middle Eastern energy policies, goals, and initiatives while aligning with European comparisons. This work also highlights technical, regulatory, and economic challenges in those regions. The main objectives of the research are to ensure that residential service sizes are optimized to maximize the economic and environmental benefits of green energy. Furthermore, in line with SDG 7, affordable and clean energy, the focus in this study is on the development and transformation of energy systems for sustainability and creating synergies with other SDGs. The paper presents insights on the European Directive, including the amended Renewable Energy Directive (RED II and III), to recommend policy enhancements and regulatory changes that could strengthen the growth of RECs in Asian countries, Pakistan, and the Middle East, paving the way for a more inclusive and sustainable energy future. Additionally, it addresses the main causes that hinder the expansion of RECs and SGs, and offers strategic recommendations to support their development in order to reduce dependency on national electric grids. To perform this, a perspective study of Pakistan’s indicative generation capacity by 2031, along with comparisons of energy capacity in the EU, the Middle East, and Asia, is presented. Pakistan’s solar, wind, and hydro potential is also explored in detail. This study is a baseline and informative resource for policy makers, researchers, industry stakeholders, and energy communities’ promoters, who are committed to the task of promoting sustainable renewable energy solutions. Full article

Despite substantial growth in eco-innovation (EI) research, most studies rely on cross-sectional data, limiting understanding of the temporal dynamics of EI and its determinants under varying macroeconomic conditions. This study addresses this gap by analysing panel data from Spanish manufacturing firms across three phases of the business cycle: pre-crisis expansion (2004–2007), the global financial crisis (2008–2013), and recovery (2014–2016). We investigate the drivers of two distinct types of eco-innovation: efficiency EI (energy and material savings) and environmental EI (reducing environmental harm), focusing on the role of regulation, institutional interventions, and firm-level innovation capacities. Using a random-effects panel probit model that accounts for unobserved firm heterogeneity, we examine how these drivers operate across different macroeconomic contexts. Our findings reveal that regulation consistently fosters EI, while the influence of subsidies, R&D capacity, and collaborative networks is more context-dependent, particularly during economic downturns. The results highlight the cumulative, path-dependent, and cyclical nature of EI, providing novel insights into the conditions that enable firms to sustain green innovation over time. Drivers of eco-innovation differ systematically between efficiency- and environment-oriented strategies, and these differences remain stable over the business cycle, implying distinct underlying mechanisms and policy implications. Accordingly, policy design—particularly during economic downturns—should distinguish between reinforcing incentives for internal efficiency improvements and sustaining regulatory and financial support for environmental EI. Full article

Rapid economic growth has directly driven up energy demand, and the gradual depletion of traditional fossil fuels has severely hindered sustainable development. Developing green and efficient geothermal exploitation technologies constitutes a crucial measure for tackling this sustainable development issue. This paper presents a field test associated with a clean energy system conducted in the Guanzhong Basin, China, with the core component of a coaxial casing deep geothermal well. A distributed temperature sensing system (DTS system) with over 3000 m-depth optical fiber installed and adopted to monitor near-wellbore formation temperature changes. Combining information on the inlet/outlet water temperature and flow rate monitored by an integrated temperature–pressure monitoring system, the heat transfer patterns during the operation of the deep geothermal well are deeply investigated. The research results demonstrate that a higher operation parameter of flow rates has a significant increasing effect on the heat transfer capacity of heat exchangers for coaxial casing deep geothermal wells. Although the increase in inlet temperature has minimal effect on the outlet temperature, it leads to a continuous decline in heat transfer capacity. In addition, as heat exchange duration extends, the geothermal gradient of the near-wellbore formation progressively declines. Full article

Driven by the national policy of total water resources control and efficiency improvement, the behavior of water resource utilization reduction by firms is widespread, which may have an impact on the value of firms. This study integrates dynamic capability theory and signaling theory to construct a dual-path analytical framework, systematically investigating the impact of water utilization reduction on firm value and its intrinsic mechanisms. Based on data from Chinese A-share listed companies spanning 2012–2023, fixed-effect models, mediation-effect tests, and heterogeneity analysis are employed for empirical verification. The results reveal that water utilization reduction exerts a significant dual-path promoting effect on firm value: it enhances financial performance (ROA) primarily through technological innovation, reflecting the process of resource orchestration and dynamic capability construction; concurrently, it boosts market performance (Tobin’s Q) mainly by improving ESG performance as a signaling channel, mirroring the capital market’s positive pricing of green signals. Further heterogeneity analysis indicates that these effects are more pronounced during the policy deepening stage, in non-water-intensive industries, and in humid/sub-humid regions. This study contributes theoretical support and empirical evidence for firms’ green transformation and the formulation of differentiated water resource policies by the government, highlighting the synergistic development of high-quality economic growth and ecological civilization construction. Full article

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

This paper examines how logistics performance conditions the relationship between trade openness, economic growth and per capita CO₂ emissions in Mediterranean economies. Using an unbalanced panel of 20 countries over the period 2007–2022, we combine static fixed-effects, dynamic panel generalized method of moments (GMM) estimators and Method-of-Moments Quantile Regression (MM-QR). CO₂ emissions per capita, the World Bank Logistics Performance Index (LPI), trade openness and GDP per capita are drawn from World Bank databases, and interaction terms between LPI and both income and trade openness are constructed to capture conditional effects. The results from fixed-effects and system GMM estimations show that logistics performance exerts a robust and statistically significant negative effect on emissions, whereas GDP per capita is a positive driver and trade openness tends to reduce emissions when logistics capacity is sufficiently strong. Negative and significant interaction terms between LPI and both income and openness indicate that logistics efficiency amplifies the environmental benefits of trade and growth. Quantile regressions reveal that these patterns are most pronounced in high-emission countries, where improvements in logistics performance and its interaction with trade and income generate larger marginal reductions in CO₂ emissions. Overall, the findings highlight the central role of logistics modernization and green trade facilitation in reconciling trade-led growth with decarbonization in the Mediterranean Basin. From a policy perspective, the evidence suggests that prioritizing green logistics and trade facilitation—particularly in high-emission Mediterranean economies—can yield the largest marginal reductions in CO₂ emissions. Full article

The accounting and analysis of agricultural grey water footprint (AGWF) are crucial for building a low-water-consumption agricultural production model and improving water resource efficiency in Fujian Province. This study innovatively integrated green development indicators into an extended STIRPAT model, quantitatively analyzed the drivers of AGWF from six dimensions (population, economy, technology, dietary structure, meteorology, and green development) based on data from 2009 to 2023. The results indicated that the AGWF in Fujian Province exhibited an overall upward trend, increasing from 114.61 billion m³ to 221.30 billion m³. Population expansion (elasticity: 0.49853) and economic growth (elasticity: 0.46329) were identified as the primary positive drivers, while technological progress exerted a mitigating effect (elasticity: −0.07253). The impacts of dietary structure, precipitation, and green development measures, though statistically significant, were quantitatively limited within the study period (elasticities of 0.0312, 0.0273, and 0.004, respectively). These findings provide quantitative support for formulating targeted policies for agricultural water resource management and non-point source pollution control in regions with similar characteristics. Full article

The Yellow River Basin (YRB), a typical river system facing the challenge of balancing ecological conservation and economic development, offers valuable insights for global sustainable watershed governance through its forestry green transformation. Based on panel data from nine provinces in the basin from 2005 to 2022, this study constructs an efficiency evaluation indicator system for forestry green development. This system incorporates four inputs (labor, land, capital, and energy), two desirable outputs (economic and ecological benefits), and three undesirable outputs (wastewater, waste gas, and solid waste). By systematically integrating the undesirable outputs-based super-SBM model and the global Malmquist–Luenberger (GML) index, this study provides an assessment from both static and dynamic perspectives. The findings are as follows. (1) Forestry green development efficiency showed fluctuations over the study period, with the basin-wide average remaining below the production frontier. Spatially, it exhibits a pattern of “downstream > upstream > midstream”. (2) The average GML index is 0.984 during the study period, representing an average annual decline in forestry green total factor productivity of 1.6%. The growth dynamics transitioned from a stage dominated solely by technological progress to a dual-driver model involving both technological progress and technical efficiency. (3) The drivers of forestry green total factor productivity growth in the basin show profound regional heterogeneity. The downstream region demonstrates a synergistic dual-driver model of technical efficiency and technological progress, the midstream region is trapped in “dual stagnation” of both technical efficiency and technological progress, and the upstream region differentiates into four distinct pathways: technology-driven yet foundationally weak, efficiency-improving yet technology-lagged, endowment-advantaged yet transformation-constrained, and condition-constrained with efficiency limitations. The assessment framework and empirical findings established in this study can provide empirical evidence and policy insights for basins worldwide to resolve the ecological-development dilemma and promote forestry green transformation. Full article

This study examines the association between financial structure components—financial access, depth, and efficiency—technological innovation, and environmental pressure in the European Union over the period 1992–2021, with the EU energy transition serving as the broader policy context. To capture the multidimensional nature of environmental pressure, a composite Environmental Pressure Index (EPI) is constructed using Principal Component Analysis (PCA), integrating indicators of air pollution, biocapacity, ecological footprint, and income-related economic activity. Employing a Pooled Mean Group (PMG) estimator within a panel ARDL framework, the results indicate that financial access is positively associated with environmental pressure in both the short and long run, whereas financial depth and financial efficiency are linked to lower environmental pressure over the long term. Technological innovation exhibits a time-varying relationship: innovation-related activities are associated with higher environmental pressure in the short run, reflecting transitional adjustment costs, but with reduced pressure in the long run as cleaner and more efficient technologies diffuse. Urbanization and population growth are also found to contribute positively to environmental pressure, pointing to persistent demographic challenges within the EU. From a policy perspective, the findings highlight the importance of aligning financial governance with the objectives of the European Green Deal by incorporating environmental efficiency considerations into credit allocation, supporting innovation-oriented investments, and promoting integrated spatial and environmental planning. Overall, the study suggests that coordinated financial development and innovation policies can contribute to mitigating environmental pressure in the European Union over time. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

Using Shanghai as a case study, this paper estimates multi-sector urban carbon emissions by integrating multi-source statistical data from 2000 to 2023 with IPCC guidelines. Via rolling-window time-series validation, XGBoost is the most reliable model. To better understand the underlying drivers, explainable machine-learning approaches, including SHAP and the Friedman H-statistic, are applied to examine the nonlinear effects and interactions of population scale, industrial energy efficiency, investment structure, and infrastructure. The results suggest that Shanghai’s emission pattern has gradually shifted from a scale-driven process toward one dominated by structural change and efficiency improvement. Building on an incremental framework, four scenarios, Business-as-Usual, Green Transition, High Investment, and Population Plateau, are designed to simulate emission trajectories from 2024 to 2060. The simulations reveal a two-stage pattern, with a period of rapid growth followed by high-level stabilisation and a weakening path-dependence effect. Population agglomeration, economic growth, and urbanisation remain the main contributors to emission increases, while industrial upgrading and efficiency gains provide sustained mitigation over time. Scenario comparisons further indicate that only the Green Transition pathway supports early peaking, a steady decline, and long-term low-level stabilisation. Overall, this study offers a data-efficient framework for analysing urban carbon-emission dynamics and informing medium- to long-term mitigation strategies in megacities. Full article