Search Results (2,907)

To reconcile the intensifying trade-off between chronic water scarcity and escalating forage demand in the Yellow River Basin, this study optimized integrated irrigation and fertilization regimes for silage maize. Leveraging the AquaCrop model, validated by 2023–2024 field experiments and a 35-year (1990–2024) meteorological dataset, we systematically quantified the impacts of multi-factorial water–fertilizer–heat stress under drip irrigation with mulch (DIM) and shallow-buried drip irrigation (SBDI). Model performance was robust, yielding high simulation accuracy for soil moisture (RMSE < 3.3%), canopy cover (RMSE < 3.95%), and aboveground biomass (RMSE < 4.5 t·ha⁻¹), with EF > 0.7 and R² ≥ 0.85. Results revealed distinct stress dynamics across hydrological scenarios: mild temperature stress predominated in wet years, whereas severe water and fertilizer stresses emerged as the primary constraints during dry years. To mitigate these stresses, a medium fertilizer rate (555 kg·ha⁻¹) was identified as the stable optimum, while dynamic irrigation requirements were determined as 90, 135, and 180 mm for wet, normal, and dry years, respectively. Comparative evaluation indicated that DIM achieved maximum productivity in wet years (aboveground biomass yield 70.4 t·ha⁻¹), whereas SBDI exhibited superior “stable yield–water saving” performance in normal and dry years. The established “hydrological year–irrigation method–threshold” framework provides a robust decision-making tool for precision management, offering critical scientific support for the sustainable, high-quality development of livestock farming in arid regions. Full article

Near-infrared organic photodetectors (NIR-OPDs) are emerging as versatile platforms for flexible and low-cost optical sensing, yet achieving high-performance in the NIR region remains difficult remains challenging due to intrinsic trade-offs at both the material and device levels, due to the inherent balance required among bandgap narrowing, exciton dissociation, charge transport, and dark-current suppression. This review provides a concise overview of OPD operating mechanisms and the performance metrics governing sensitivity and noise. We highlight recent molecular-engineering strategies—core fluorination, asymmetric π-bridge design, fused-ring rigidification, and polymer backbone/side-chain tuning—that effectively enhance intermolecular ordering, reduce energetic disorder, and extend NIR absorption. Progress in all-polymer detectors and ambipolar phototransistors further demonstrates improved stability and broadened detection capability. Additionally, emerging applications, including NIR communication, biosignal monitoring, flexible imaging, and biometric recognition, showcase the expanding utility of NIR-OPDs. Remaining challenges include pushing detection beyond 1200 nm, simplifying synthesis, and improving long-term stability. Overall, advances in low-bandgap molecular design and device engineering continue to accelerate the practical adoption of NIR-OPDs. Full article

Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate–distortion–perception (RDP) trade-off for image compression, a setting in which reconstructions must be not only accurate but also perceptually faithful. Our analysis is carried out through the lens of randomized distributed function computation (RDFC) framework, which provides a principled means of synthesizing randomness and shaping output distributions. Leveraging this framework, we establish finite-blocklength characterizations of the RDP region, quantifying how communication rate, distortion, and perceptual fidelity interact in non-asymptotic regimes. We further broaden this characterization by incorporating two practically relevant extensions: (i) scenarios in which encoder and decoder share side information, and (ii) settings that require strong secrecy guarantees against adversaries, which might include those with quantum capabilities. Moreover, we identify the corresponding asymptotic region under a perfect realism constraint and examine how side information, finite blocklength effects, and secrecy demands influence achievable performance. The resulting insights provide actionable guidance for the development of low-latency, secure, and realism-aware image compression and generative modeling systems. Full article

Understanding residents’ perceptions of ecosystem services (ES) and ecosystem disservices (EDS) is crucial for protected areas governance. This study, conducted in China’s Three-River-Source National Park (TNP), employed participatory rural appraisal and household questionnaires to examine local cognitive patterns of ES and EDS, along with their socio-spatial heterogeneity and perceived synergies and trade-offs among them. The key findings are as follows: (1) Cultural services received the highest scores, followed by regulating services, whereas provisioning services, especially food provisioning, were rated as relatively inadequate. Safety threats were considered the most severe EDS. Overall, a Matthew Effect emerged: services with high current perception scores showed an improving trend, while those with low scores deteriorated. (2) Spatially, ES/EDS evaluation scores exhibited a “core zone < general control zone < peripheral zone” gradient. Socio-demographic and economic factors also influenced residents’ perceptions; women and the elderly were especially more concerned about food and energy supply shortages and safety issues. (3) The relationships among the various ES and EDS are primarily synergistic rather than trade-offs. Specifically, gains in regulating services were associated with enhanced cultural services, while declines in provisioning services and intensified safety threats coincided with the deterioration of material EDS. These findings offer a scientific basis for managing protected areas in high-altitude, ecologically fragile regions and provide practical insights for balancing ecological conservation with community development. Full article

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While the economic consequences of regional regulatory disparities are well-documented, the specific impacts of this regulatory convergence remain insufficiently explored. To address this gap, this study constructs a novel index to measure the convergence of environmental regulations between urban districts and rural counties at the prefecture level. Utilizing an unbalanced panel dataset of 5600 county-level timber processing enterprises, the Heckman two-stage model is employed for empirical analysis. The results demonstrate that the convergence of urban and rural environmental regulations significantly enhances both the export probability and export intensity of county-level firms, with these effects exhibiting persistence and cumulative growth over time. These findings remain robust across a series of validation tests, including instrumental variable estimation, double machine learning, and alternative model specifications. Mechanism analysis reveals that regulatory convergence promotes exports primarily by improving access to green credit and enhancing peer quality within the industry. Furthermore, heterogeneity tests indicate that the positive effects are most pronounced for start-ups and firms in the decline stage, as well as for enterprises located in eastern China, those outside the Yangtze River Economic Belt, and those subject to minimal government intervention. This study provides critical micro-level evidence that helps enterprises navigate the evolving policy landscape and supports the formulation of strategies to boost export trade amidst the integration of environmental regulations. Full article

This study examines the potential implications of the EU–Mercosur free trade agreement for the Polish horticultural sector, with particular emphasis on sustainability, trade competitiveness, and structural complementarities between the regions. Drawing on production, trade, and demographic data for the EU, Poland, and Mercosur countries, the analysis evaluates the alignment of horticultural supply and demand structures, the degree of intra-industry exchange, and the economic conditions shaping bilateral trade. The research applies the Grubel–Lloyd index and a Poisson Pseudo-Maximum Likelihood (PPML) gravity model to assess the determinants of Poland’s horticultural exports to Mercosur. The results indicate that trade remains predominantly inter-industry, reflecting substantial differences in agricultural specialisation and regulatory frameworks. At the same time, rising income levels in Mercosur, together with selected product-level complementarities, indicate emerging export opportunities for Poland. Poland’s trade with the Southern Common Market remains mainly as inter-industry, with the greatest export potential concentrated in high-value-added processed goods. Divergent sustainability standards, particularly in pesticide use, environmental regulation, and carbon-intensive transport, pose structural challenges that may affect the competitiveness and environmental footprint of expanded trade. Overall, the findings provide evidence that closer integration with Mercosur may support export diversification, but requires careful alignment with the EU’s sustainability objectives to ensure resilient and environmentally responsible development of the horticultural sector. Full article

This study presents an experimental 3E (energy–exergy–environmental) assessment of a PLC-controlled solar air heater (SAH) equipped with adjustable internal baffles. Unlike conventional passive systems, the proposed design enables active airflow regulation to maintain stable outlet temperatures of 54 °C and 60 °C, achieving rapid stabilization within 3–10 s under outdoor conditions. Experimental results show that increasing the baffle inclination significantly enhances convective heat transfer and thermal efficiency, while the friction factor remains primarily governed by the Reynolds number and exhibits minimal sensitivity to baffle angle. Exergy efficiency values remain relatively low (1.24–2.69%), and the sustainability index stays close to unity, reflecting the inherent thermodynamic limitations of low-temperature solar air heaters rather than deficiencies in system design. A regression-based airflow velocity model is developed to support fan-speed optimization and to clarify the trade-off between thermal enhancement and auxiliary power demand. Long-term projections based on regional solar data indicate that the proposed SAH can deliver approximately 20–22 MWh of useful heat and mitigate nearly 9 tons of CO₂ emissions over a 20-year operational lifetime. Overall, the results demonstrate that PLC-assisted dynamic baffle control provides a flexible and effective approach for improving the performance and operational stability of solar air heaters for low-temperature drying applications. Full article

To address climate change and advance environmental sustainability in the context of the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), China has actively promoted digital trade development under its carbon peaking and carbon neutrality goals. However, whether digital trade contributes to environmental improvement, and through which mechanisms it does this, remains an open empirical question. This study examines whether and how digital trade development affects environmental pollution in China, with particular emphasis on spatial spillover effects and underlying mechanisms. Using provincial panel data from 2009 to 2023, we employ a spatial Durbin model combined with a mediation analysis framework. The results show that digital trade development has increased steadily in China and significantly reduces local environmental pollution, indicating a clear green effect. The spatial Durbin model shows that the environmental benefits of digital trade are unevenly distributed across space, with pollution reductions in core regions accompanied by increased emissions in neighboring areas. Further mechanism analysis indicates that industrial structure upgrading and consumption structure transformation are key channels through which digital trade development improves environmental sustainability. These findings provide important insights for coordinating digital trade expansion with regional environmental governance and low-carbon transition strategies. Full article

Phloeosinus cupressi Hopkins is an invasive bark beetle that poses a serious threat to Cupressus trees, with potential ecological and economic impacts globally. Native to North America, it has spread to Australia and New Zealand, and climate change may further alter its range. Global trade increases the risk of spread, highlighting the need for predictive modeling in management. In this study, we employed CLIMEX and random forest (RF) models to project the potential global distribution of P. cupressi, incorporating host distribution data for Cupressus. Climatic suitability is concentrated in temperate, subtropical, and Mediterranean zones, including Europe, the U.S., South America, China, Australia, and New Zealand, totaling 10,165.22 × 10⁴ km². Coldest-quarter precipitation (bio19) and annual temperature range (bio7) were identified as the most influential variables. Under RCP6.0 scenarios, suitable areas are projected to expand northward, increasing by ~18%. Regional shifts include contraction in southern Europe and South China, expansion in southern Argentina, southeastern Australia, and coastal New Zealand. Temperature sensitivity is expected to exceed precipitation, enhancing colonization. Due to global Cupressus trade, quarantine and monitoring should focus on high-risk regions. Our findings support early detection, long-term monitoring, and control measures for managing P. cupressi under climate change. Full article

The Three Gorges Reservoir Area (TGRA) faces mounting pressures from urbanization and hydrological regulation, threatening the sustainability of its ecosystem services (ESs). The InVEST model, coupled with optimal parameter geographical detector (OPGD) and geographically and temporally weighted regression (GTWR), was employed to assess spatiotemporal changes, trade-offs/synergies, and driving mechanisms of four ESs, water yield (WY), habitat quality (HQ), carbon storage (CS), and soil conservation (SC), from 2000 to 2020. Results revealed that WY and SC increased significantly by 24.54% and 5.75%, respectively, while HQ declined by 3.02% and CS remained relatively stable, with high-value ES zones mainly concentrated in the eastern and northern forest-dominated areas. Regarding interactions, strong synergies existed among HQ, CS, and SC, whereas WY exhibited persistent trade-offs with other services, particularly in the central agricultural-urban transitional zone. Furthermore, landscape diversity increased linearly, driven by forest expansion and urban growth. Mechanistically, land use type (LUT) dominated the spatial distribution of WY, HQ, and CS, while slope primarily controlled SC patterns, with all driver interactions demonstrating enhanced effects. By coupling OPGD with GTWR, this study uniquely elucidates the spatiotemporal instability of ES trade-offs/synergies and the spatial heterogeneity of their driving mechanisms, providing a novel scientific basis for implementing spatially differentiated management strategies in large-scale reservoir-impacted regions. Full article

Minimum wage is an important tool for reducing income inequality and supporting social welfare. Consequently, governments around the world have established minimum wage systems. As such, minimum wage policies connect distributive justice with the economy’s capacity to sustain broad-based welfare over time, placing the equity–efficiency trade-off at the center of societal sustainability. However, the micro-level impact of the minimum wage system on firms has always been an important topic for scholars. This study uses panel data from listed Chinese manufacturing firms over a period from 2005 to 2021 to construct an indicator of the minimum wage standards implemented in the firm locations. Employing the multiple linear regression model, this paper empirically examines the effects of minimum wage on labor productivity. The empirical findings demonstrate that minimum wage significantly reduced the sample firms’ labor productivity. Moreover, the negative impact of the minimum wage was primarily concentrated among non-state-owned firms, labor-intensive firms, firms operating in industries characterized by intense product market competition, firms situated in regions with strong legal protections, firms with comparatively low average employee wages, and export-oriented firms. Subsequently, this study delves into the mechanism through which minimum wage negatively affects labor productivity. We find that implementation of minimum wage leads to a reduction in corporate investment, indicating that there is no significant substitution relationship between capital and labor. These adjustment margins provide microfoundations through which statutory wage floors can influence the resilience and inclusiveness of development, indicating that the pace and design of wage increases should balance income protection with the preservation of productive capacity to support sustainable human development—grounded in steady productivity growth, equitable income distribution, and stable firm investment. Our findings contribute to a better understanding of the mechanism through which minimum wage affects labor productivity in theory, while concurrently furnishing policy insights for the optimization of the minimum wage system and maintaining sustainable societal development in practice. Full article

Ecological compensation is an important policy tool for coordinating ecological protection and economic development and narrowing regional disparities. In China, Linyi, for the first time, applied a cap-and-trade policy to the forestry sector by implementing the Intergovernmental Forest Ecological Indicator Trading Policy (IFEITP)—a new ecological compensation policy—to increase the city’s overall forest coverage. However, the compensation standard for this policy was formulated solely by referring to provincial afforestation subsidy standards, resulting in excessively low indicator trading prices and making the policy difficult to sustain. This paper proposes a technical framework for ecological compensation based on the ecosystem service spillover value (ESSV), aiming to optimize the IFEITP. The results revealed that during the policy implementation period, Linyi’s total ecosystem service value (ESV) increased, and the proportion of ESV provided by forests in each district and county also increased. Under the new framework, there were minor changes in the ecosystem service supply zones and payment zones. The compensation received by supply zones increased by 116.2%, whereas the payments made by payment zones accounted for less than 0.2% of local fiscal revenue. The newly calculated indicator trading price under this framework not only reflects the value of ecosystem services but also remains within the acceptable range of government finances, demonstrating high operability and providing a basis for optimizing the IFEITP. This study offers broader insights for regions with similar ecological and socioeconomic conditions, enabling the application of analogous ecological compensation policies to maintain environmental justice and promote sustainable development. Full article

Against the backdrop of global climate governance and China’s “dual carbon” goals, carbon emissions trading (CET) has become a core policy instrument for promoting low-carbon transformation. However, it remains unclear whether CET policies can effectively improve corporate carbon performance and, more importantly, through which micro-level mechanisms such effects operate within firms. To address these gaps, this study applies a difference-in-differences (DID) approach to examine the impact of CET policy on corporate carbon performance and its transmission pathways. The results show that CET policy significantly enhances corporate carbon performance. Heterogeneity analysis further reveals that this positive effect is more pronounced in regions with lower environmental governance intensity, and that the policy’s effectiveness strengthens over time. Mechanism tests indicate that financing constraints and R&D investment serve as chain mediators: CET policy alleviates financing constraints, stimulates R&D investment, and thereby improves carbon performance. Moreover, the moderating effect analysis shows that executives’ green backgrounds reinforce the policy’s effectiveness by further easing financing constraints and mitigating their negative impact on R&D investment. Overall, these findings deepen the micro-level understanding of market-based environmental regulation and provide policy implications for optimizing CET policy design, improving resource allocation efficiency, and fostering low-carbon transformation and sustainable competitive advantages for enterprises. Full article

The previous studies have suggested that the cap-and-trade carbon emissions trading scheme (ETS) was effective in reducing greenhouse gas emissions and atmospheric pollution. Are there other environmental benefits of this policy? This research question remains unanswered in the literature. Our study reports that China’s carbon ETS significantly improved the land surface ecological quality (LSEQ). The study analyzes the data of 328 Chinese cities during 2005–2020. A difference-in-differences (DID) regression model is used for quantitative policy evaluation. The land surface ecological quality is measured by a synthetic indicator of the remote sensing ecological index (RSEI). There are three main findings. (1) On average, the carbon ETS improved the land surface ecological quality index by 0.0113, which contributed 51% of the ecological quality improvement in ETS-implementing regions in the post-policy period. The positive effect of the policy increased over time. (2) The implementation of the carbon ETS reduced pollution emissions, promoted green innovation, and expanded the share of land with natural vegetation coverage. These phenomena provide explanations for why the policy improved the land surface ecological quality. (3) The policy effect exhibited some heterogeneities contingent on local climatic conditions. The effect was stronger in regions with more precipitation, shorter sunlight duration, and higher temperature. Full article

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article