Search Results (1,662)

Faced with increasingly severe resource shortages and environmental pressures, exploring the impact of the digital economy on green and low-carbon development and its potential mechanisms is of great significance. Drawing on a comprehensive panel dataset spanning the decade from 2014 to 2023, this study examines 11 provincial administrative regions situated within the Yangtze River Economic Belt in China, systematically examining the effects and underlying pathways of the digital economy on green and low-carbon development. We construct an evaluation index system for the digital economy and green and low-carbon development, and use a two-way fixed effects model, a moderating effect model, and a threshold regression model for empirical analysis. Empirical results show that the digital economy significantly promotes green and low-carbon development, and this conclusion remains robust after a series of robustness tests. Mechanism analysis indicates that green technology innovation plays a significant moderating role, amplifying the environmental benefits of the digital economy; industrial structure upgrading exhibits a double threshold effect, with the promoting effect of the digital economy on green and low-carbon development increasing as the threshold is exceeded. Heterogeneity analysis shows that the ecological effects of the digital economy are significant in the midstream and southwest cluster and in areas with high factor allocation efficiency. We conclude that optimizing the environment for digital economic development, emphasizing innovation in digital green technologies, and implementing differentiated regional and structural policies can achieve a coordinated advancement of digital transformation and green and low-carbon development, providing valuable empirical evidence and policy implications for regional sustainable development. Full article

Improving energy efficiency is critical for tackling environmental issues and achieving sustainable development. Understanding how digital technology affects energy efficiency and its underlying mechanisms can deepen our comprehension of the economic consequences of digital innovation. This study adopts a dictionary-based method to identify digital technology patents from a large-scale patent dataset and employs a comprehensive evaluation approach incorporating both subjective and objective weights to measure digital technology advancement. Building on this framework, the research uses city-level data from China and applies panel data models alongside mediation effect models as core analytical tools to investigate the impact mechanisms and effects of digital technology on energy efficiency. Key findings reveal that digital technology has developed rapidly, exhibiting distinct phase-specific characteristics, especially after 2010, though notable regional disparities remain. Robust tests confirm that digital technology significantly enhances energy efficiency. Nonlinear regression results indicate that the marginal effect of digital technology changes dynamically across different stages of energy efficiency development. Heterogeneity tests demonstrate that the impact of digital technology on energy efficiency exhibits typical heterogeneous characteristics. Mechanism analysis shows that digital technology enhances energy efficiency primarily through two pathways: green technology innovation and industrial structure upgrading. Further analysis suggests that regional convergence in energy efficiency is objectively present, and digital technology actively accelerates this convergence process. These findings offer practical insights to guide policymakers in designing and implementing digital technology-driven strategies aimed at enhancing energy efficiency. Full article

Within a waste biorefinery framework, integrating agro-industrial by-products into the circular economy requires a detailed understanding of the thermochemical conversion behaviour of low-grade carbonaceous materials. This study evaluates the co-pyrolysis characteristics of Soma lignite (SL) and pectin-rich sugar beet pulp (SBP) as a sustainable route for upgrading these resources into clean energy carriers. Interactions between the two feedstocks were analysed by thermogravimetric measurements, triple-region kinetic modelling, and quantitative synergy indices at six mixing ratios, including the pure samples (100:0, 80:20, 60:40, 40:60, 20:80, and 0:100 wt% SL:SBP). The Reactivity Index ($R_{\mathrm{m}}$) increased from $0.97\times {10}^{-4}$ s${}^{-1}$K${}^{-1}$ for pure SL to $8.65\times {10}^{-4}$ s${}^{-1}$K${}^{-1}$ for the 20:80 blend, showing that SBP acts as a highly reactive biomass component that accelerates devolatilisation in the main pyrolysis region. Synergy analysis indicated a shift from inhibitory behaviour in coal-rich blends to slightly positive synergy in SBP-rich mixtures, with the onset of positive $\Delta TC$ around 60 wt% SBP under the present single-heating-rate, non-replicated TGA conditions. This tentative threshold-like behaviour suggests that a critical level of literature-supported, hypothesised hydrogen-donating biomass radicals may be required to overcome the structural resistance of the coal matrix. Within these experimental limitations, the apparent macro-kinetic deviations and first-order Arrhenius parameters suggest that SL/SBP co-pyrolysis follows a complex, non-additive pathway that should be further validated by multi-heating-rate and product characterisation studies in future work. The primary contribution of this work lies in proposing this distinct threshold-like biomass fraction at the macro-kinetic level that governs the transition from heat-transfer-limited antagonism to radical-influenced synergy in low-rank coal and pectin-rich biomass blends. Overall, the combined $\Delta TC$, $\Delta E$ and $R_{\mathrm{m}}$ descriptors provide useful macro-kinetic benchmarks for guiding the optimisation of thermochemical processes for low-grade carbonaceous resources. Full article

The present paper is aimed at studying the convergence of the Yamabe flow in the case of noncompact solitons. The more specified example of locally conformally flat noncompact solitons is addressed with the aim to newly analyse the qualities of the Ricci scalar. The particular case of noncompact pseudo-Riemannian solitons is studied; moreover, in the instances of Schwarzschild and Generalized-Schwarzschild geometries, rescalings of spherically symmetric weights are performed. For this purpose, new results are achieved as far as the considered structures are concerned. The Myers Theorem is upgraded as the new Myers paradigm of spacetime-dimensional manifolds, where the Einstein Field Equations can now be taken into account. In particular, the Myers Theorems are studied here as far as their new implementation in General Relativity Theory is concerned. As a first important result, the Myers mean curvature is found to coincide with the Ricci scalar in General Relativity Theory, where the 4-position of the observer, from which the 4-velocity 4-vector is calculated from, is taken as that of the observer solidal with the reference frame of the photon. The following results are also of relevance. In more detail, the umbilicity conditions are applied. At a further step, the role of the umbilicity conditions in GR after the Myers Theorems are studied for weighted manifolds and specific new implications of weighted manifolds are developed. The description of the weighted Schwarzschild manifolds and that of the weighted Generalized-Schwarzschild manifolds are newly studied as follows: as a new finding, the Birkhoff Theorem is newly reconciled with the rotational ansatz of the metrised solitons, and the comparison with the previous results about the Brendle non-metrised solitons is accomplished with the outcome stressing the new roles of the new rescalings of the metric tensor with respect to the previous known results of the scaling of the metric tensor of the non-metrised solitons. In the present framework, these procedures allow one to prove the reconciliation of the EFEs with the Yamabe flow. The flow on the tipping lightcones is newly written. The umbilicity condition is studied in General Relativity after the upgrade of the Myers Theorems as far as the sectional curvatures are concerned; as a result, the Calabi–Bernstein description is implemented in General Relativity, as well as the Chen–Yau requirements, and the cases of weighted manifolds are taken into account. More specifically, the equal-time 2-dimensional space surfaces are studied analytically, onto which the weighted General-Relativistic solitons which satisfy the Einstein field equations after the Yamabe flow are projected due to the rotational ansatz. As an accessory introductory result, the class of Wu non-metrised solitons are proven to be discarded in several aspects of the Wu description as the conditions provided after the work of Wu are not compatible with metrisation. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article

This paper examines how rural development support was associated with changes in Lithuania’s agri-food sector between 2000 and 2025 across successive Common Agricultural Policy (CAP) programming periods. Integrating complementary theoretical perspectives, the study assesses whether policy interventions were linked to structural transformation, market upgrading, partial innovation deepening, and sustainability-oriented change, or whether they primarily reinforced existing agri-food development paths. Methodologically, the research employs a quantitative, longitudinal, descriptive–analytical design, combining time-series analysis with comparative policy-cycle analysis. By tracing both incremental adjustments and more pronounced structural shifts over the 2000–2025 period, the paper provides an evidence-based assessment of how rural development support aligned with sectoral change. The findings suggest that the observed trajectory is most consistent with modernization, consolidation, and market upgrading, while innovation-led transformation appears more uneven and concentrated in downstream processing than in primary agriculture. The results contribute to debates on the calibration of rural development instruments and offer implications for future policy design in small open economies undergoing agri-food restructuring. Full article

In the context of global efforts to achieve carbon neutrality, understanding how digital–intelligent integration influences carbon emissions is crucial for advancing the ecological transition. Using panel data from 30 provincial-level regions in China (2014–2023), a digital–intelligent integration index was constructed via entropy weighting and a coupling coordination model. Employing fixed-effects, mediation, and spatial Durbin models, the analysis shows that digital–intelligent integration is significantly associated with lower carbon intensity, a result that is robust to endogeneity concerns and alternative specifications. Industrial structure upgrading and green technology innovation were identified as mediating pathways. Furthermore, digital–intelligent integration generates positive spatial spillovers, reducing carbon intensity in neighboring provinces. Notably, these spillovers are geographically constrained and vary significantly across the regions. These findings indicate the need to formulate regionally differentiated strategies to harness the specific mechanisms through which digital–intelligent integration operates in different contexts. Full article

The natural period is a key parameter in seismic design, but current empirical code formulas act as lower bounds for design safety, making them overly conservative for the precise performance assessment of existing buildings. To derive an optimal best estimate of the actual dynamic behavior, this study proposes a novel methodology based on 283 measured data points worldwide. Overcoming the limitations of conventional single-variable models, this study introduces story height as a physical proxy variable alongside data clustering techniques. Story height extends beyond simple geometry, indirectly representing mass distribution and structural stiffness design levels, thereby effectively controlling the dispersion of heterogeneous global data on physical grounds. Consequently, the proposed piecewise bivariate non-linear regression model achieved a significantly lower RMSE across all structural systems compared to existing design codes and single-variable models, substantially improving prediction accuracy. Unlike traditional fixed-constant approaches, this continuously upgradable framework can serve as a robust foundational model for large-scale seismic screening in smart cities and digital twin-based maintenance systems. Full article

Expanded polystyrene (EPS) waste is a major environmental concern, yet practical routes to upgrade it into higher value-added materials remain limited. Here, post-consumer EPS was dissolved in ethyl acetate and solvent-cast into films containing cellulose nanocrystals (CNCs) and a sunflower oil. Three formulations were produced: F-EPS (100% EPS), F-EPS + CEL (80% EPS/20% CNC), and F-EPS + CEL + OIL (80% EPS/15% CNC/5% oil). CNC markedly enhanced optical shielding, reducing transmittance at 400 nm from ≈58% (F-EPS) to ≈18% (CNC containing films). All films remained hydrophobic, showed negligible water uptake, and exhibited low mass loss after 30 days of accelerated weathering ($\Delta M=1$–$3\%$). Tensile testing showed that F-EPS had the highest UTS and elongation at break (10.0 ± 0.6 MPa and 10.5 ± 0.4%), whereas adding cellulose increased the elastic modulus (249.5 ± 29.0 MPa to 358.4 ± 64.8 MPa) but reduced UTS and elongation (8.2 ± 0.2 MPa and 5.4 ± 2.5%). Oil addition led to a further reduction in UTS and elongation (6.2 ± 0.4 MPa and 3.6 ± 0.0%), while the modulus returned to a value statistically similar to neat F-EPS. FTIR and XRD confirmed preservation of the EPS chemical fingerprint and a predominantly amorphous structure ($2\theta \approx 20$–30°). Overall, EPS + CNC + OIL films combine hydrophobicity, UV-screening, and elastic modulus similar to neat F-EPS, supporting their use as moisture-resistant, UV screening protective topcoats for non-food-contact paperboard packaging. Full article

Against the dual backdrop of the global drive toward carbon peaking and carbon neutrality, a core pillar of the United Nations Sustainable Development Goals (SDGs), and the accelerated integration of new-generation digital technologies into sustainable production practices, this study employs a micro-level perspective to systematically explore how AI innovation optimizes organizational, production, and investment structures to enable corporate low-carbon development. The study sample comprises 21,428 firm-year observations from Chinese A-share listed manufacturing companies over the period of 2010–2022. The results show that AI innovation can significantly reduce corporate carbon emission intensity, specifically achieving corporate low-carbon development through three paths: optimizing low-carbon organizational governance, upgrading emission-reducing production processes, and reorienting investment toward green assets. Further analysis shows that executives’ green cognition and government environmental attention together constitute dual internal and external driving forces for corporate carbon emission reduction. Heterogeneity analysis reveals that the emission reduction effect of AI innovation is more significant for enterprises with a low supply chain concentration, those in high-environmental-sensitivity industries, and those located in regions with underdeveloped factor markets. From the micro-perspective of corporate sustainable low-carbon development, this study offers further theoretical support and empirical evidence for regulators aiming to optimize AI innovation incentives, improve sustainable environmental governance, and advance global sustainable industrial development. Full article

In the context of the global dual-carbon goals and China’s DP strategy, strengthening the coupling between digital productivity (DP) and the high-quality development of the energy industry (HQDEI) is essential for resource-based regions. Doing so can help these regions overcome transition constraints and advance green, low-carbon development. Using panel data for nine prefecture-level cities in Guizhou Province from 2014 to 2023, we construct composite indices for DP and HQDEI with an improved entropy-weight TOPSIS approach. We then characterize their spatiotemporal evolution using a coupling coordination degree (CCD) model and kernel density estimation. Finally, we examine the determinants of coupling coordination through panel regression and threshold models. The results show that: (1) The CCD between DP and HQDEI efficiency continues to increase, with regional differences displaying a periodic convergence–divergence pattern and a spatial structure characterized by core agglomeration and outward diffusion. Gradient disparities in coordinated development are evident between central and peripheral areas. (2) Consumption upgrading and fiscal self-sufficiency significantly promote CC, whereas a traditional resource-dependent growth model significantly suppresses it. Constrained by short-term adaptation and integration costs, digital innovation currently exerts a negative effect, and its enabling potential has not yet been fully realized. (3) Nonlinear tests identify a single digital-infrastructure threshold: the enabling effect of digital innovation turns positive only once infrastructure surpasses a critical level, revealing pronounced interval heterogeneity. This study advances the theoretical understanding of the bidirectional coupling between DP and HQDEI, provides empirical guidance for energy digital transformation and high-quality development in resource-based regions of western China, and offers transferable insights for green, low-carbon transitions in traditional energy regions worldwide. Full article

New energy vehicle promotion (NEVP) is of great significance for the green and low carbon development of urban transportation. Based on the panel data of new energy vehicle sales, carbon emissions, and air quality in Chengdu, China, from 2014 to 2024, this paper employs multiple linear regression, distributed lag and multiple mediation pathway models to empirically examine the environmental benefits of NEVP. A heterogeneity analysis is also conducted by integrating the distribution of charging stations across urban circles. The results show that: (1) In the multiple mediation pathway model, the total effect of NEVP includes direct effect and indirect effect. Based on the total effect, the total carbon emission from the effect of NEVP is reduced by about 3.95% of the total carbon emissions, and 40% of carbon emission within the transportation sector in Chengdu. NEVP in Chengdu has a significant direct emission reduction effect, accounting for about 39.80% of the total effect, with the annual average carbon emissions being reduced by about 432,800 tons, accounting for about 1.57% of the total carbon emissions in Chengdu. In terms of indirect effects, NEVP significantly reduces carbon emissions through three pathways: industrial structure upgrading (1.02%), green consumption transformation (1.12%), and technological innovation (0.25%). However, the benefits of NEVP on improving urban air quality are limited. (2) The lag effect analysis shows that the environmental benefits of NEVP exhibit distinct characteristics of time lag and long-term persistence. (3) The environmental benefits show significant sub-circle heterogeneity. As carbon emissions decrease, the air quality of the central urban zone (the first circle) and the suburbs (the second circle) improves significantly, while the impact on the outer suburbs (the third circle) is not significant. There is an imbalance in the layout of charging piles in Chengdu. This research offers empirical evidence and policy insights for the green and low carbon development of urban transportation. Full article

Steam Explosion (SE) is a relatively newly developed physicochemical pretreatment method that has received increasing attention since it can effectively upgrade biomass for further utilization. During SE, biomass is first exposed to high-temperature, high-pressure steam and then rapidly depressurized. This process efficiently breaks down the lignocellulosic structure, reduces moisture content, and increases fixed carbon and calorific value. It also enhances biomass grindability and densification, making it more suitable as a renewable solid fuel. This review carefully discusses the fundamental principles of SE and its effects on particle characteristics. Then, the types of SE reactors (mainly composed of batch reactors and continuous reactors) are systematically compared, and the challenges in scaling up and commercialization are discussed. Also, the characteristics of pyrolysis or gasification of biomass pretreated by SE are described in detail. Studies indicate that SE is beneficial for the enhancement of product quality. Finally, the prospects and future challenges in the development of SE (including superheated steam explosion, reaction kinetics improvement, and heat and mass transfer intensification) are presented and discussed. Full article

Routine-Biased Technological Change (RBTC) is viewed as reshaping labor markets, yet its implications for gender inequality in developing economies remain underexplored. This study examines these dynamics among formal wage workers in Indonesia from 2001 to 2019. Using stacked first-difference estimations and a dynamic shift-share decomposition, we document three interconnected patterns. First, routine displacement unfolds episodically rather than simultaneously—with relative contraction in routine cognitive jobs (2001–2005), routine manual jobs (2005–2010), and renewed routine cognitive pressures (2015–2019)—a sequence likely shaped by technological change alongside macroeconomic and institutional forces. Second, these adjustments are gender-asymmetric. Women experienced greater exposure to displacement but reallocated substantially toward non-routine interpersonal roles. This occupational upgrading is consistent with both task-based demand shifts associated with technological change and the entry of younger, more educated female cohorts. Third, employment reallocation exerted a narrowing influence on the gender wage gap, particularly in 2005–2010. However, this equalizing channel weakened over time as market valuation (wage exposure) became increasingly unfavorable to female-concentrated occupations, contributing to a renewed widening in 2015–2019. Ultimately, while residual within-task group dynamics dominate the gap’s magnitude, task-based employment and wage channels remain critical in structuring the timing and directional shifts of gender inequality in the formal sector. Full article

Amid accelerating technological change and structural transformation, advanced productivity regimes characterized by technological innovation, digital transformation, and green upgrading have become key drivers of economic restructuring. In China, this transformation is conceptualized as new quality productive forces (NQPFs). However, their implications for higher education systems remain insufficiently explored. This study examines how NQPFs influence the high-quality development of higher education. Using panel data from 30 Chinese provinces from 2015 to 2022, composite indices constructed with the entropy method are used to measure NQPFs and the high-quality development of higher education, and mediation effect models, together with a Spatial Durbin Model, are employed to analyze the underlying mechanisms and spatial interactions. The results show that NQPFs significantly promote the high-quality development of higher education. This effect operates mainly through industrial collaborative agglomeration and digital infrastructure development and also generates positive spatial spillover effects across regions. These findings highlight the role of productivity transformation in shaping the structural foundations of higher education development in the digital era. Full article