Search Results (685)

The rapid development of new-generation information technologies, such as cloud computing, artificial intelligence, big data, and blockchain, is profoundly reshaping production and lifestyles, with regional development patterns. This study employs text analysis to extract the policy adoption timeline of cloud computing from official documents and constructs a quasi-natural experiment framework. First, spatial autocorrelation and hotspot analysis reveal significant spatial dependence in the urban green total factor productivity (GTFP). Accordingly, using panel data of 284 Chinese cities from 2000 to 2023, we apply a spatial difference-in-differences (SDID) model to empirically examine the impact of cloud computing on the urban GTFP. The results show that, first, the adoption of cloud computing significantly enhances the local GTFP, but simultaneously suppresses neighboring cities’ GTFP through the siphon effect, thereby generating negative spatial spillover effects. These findings remain robust across parallel trend tests, placebo tests, and multiple robustness tests. Second, mechanism analysis indicates that improved resource allocation efficiency and strengthened green innovation are the two core channels through which cloud computing promotes GTFP. Third, heterogeneity analysis reveals that cloud computing exhibits stronger siphon effects in smaller cities, generates significant positive spatial spillover effects in coastal regions, and effectively fosters GTFP growth within urban agglomerations, while exerting limited influence on non-agglomerated areas. Moreover, industrial agglomeration further amplifies the positive impact of cloud computing on GTFP. Additionally, from the perspective of regional policies, this study finds that promoting the integrated development of urban agglomerations, reducing administrative monopoly, facilitating free factor mobility, and advancing urban international economic activities are effective pathways to mitigate the siphon effect of cloud computing on the urban GTFP. Based on these findings, this study offers targeted policy recommendations to leverage cloud computing for advancing green and high-quality urban development. Full article

To quantitatively assess the carbon emission characteristics of expressway construction and to identify its key influencing factors, this study establishes a comprehensive carbon emission accounting framework that covers the material production, transportation, and construction stages based on the life cycle assessment (LCA) approach. Typical expressway projects are selected as case studies to perform stage-based emission quantification and multivariable response analysis. The results indicate that the total carbon emissions per kilometer during the construction phase are approximately 1.80 × 10³ kg CO₂-eq/km, with material production being the dominant contributor, accounting for about 60–70%, followed by transportation and construction activities. The analysis of structural layers shows that variations in the thickness of the asphalt surface and cement-stabilized base layers, which are the main sources of emissions, are strongly and positively correlated with total emissions, making them the principal control factors. Transportation distance and equipment efficiency are identified as moderately sensitive parameters, each contributing approximately 3–5% to emission variation. Further multivariable response analysis demonstrates nonlinear coupling effects between structural parameters and transportation factors. The combined increase in layer thickness and transport distance significantly amplifies total emissions, while the marginal impact of long-distance transport gradually decreases. Based on these findings, this study proposes a low-carbon construction strategy that focuses on structural optimization, local material sourcing, energy-efficient construction practices, and the use of clean energy. The outcomes of this research provide a theoretical foundation and quantitative reference for carbon emission prediction, structural design optimization, and green construction decision making during the expressway construction phase. Full article

At present, ionic liquids (ILs) are increasingly being used to extract natural products as green solvents, but their residues can lead to risks in terms of further use for the extracted herbal materials. Therefore, it is necessary to remove them with simple and effective methods. For example, after the toxic anthraquinones in Polygonum multiflorum are removed by extraction with the IL of [C₄Bim][PTSA], it needs to be recovered and reused, and the useful stilbene glycosides should not suffer from obvious loss as they are the main functional components. In this study, an ultrasonic method with n-propanol was used to remove the residual [C₄Bim][PTSA] in the solid powders of Polygonum multiflorum that had been extracted for anthraquinones. After single-factor optimization, the removal conditions were as follows: the removal temperature was 303.15 K, the solid–liquid ratio was 1:200 (w (1 g):v (200 mL)), the ultrasonic time was 40 min, and there were four operations. Under these conditions, ILs could be completely removed with almost no loss of stilbene glycosides in solid powders. After that, the IL in the extracting solution and scrubbing solution was recovered by the back-extraction method, and an IL with high purity could be obtained for reuse. The total recovery efficiency of the IL reached more than 98%. Then gas chromatography (GC) was conducted for the determination of residual ethanol and n-propanol in the solid powders of Polygonum multiflorum, which could be used to quickly detect the contents of two organic solvents within three minutes. Besides that, the method could also be applied to the determination of residual organic solvents in the raw materials of Polygonum multiflorum, and the results showed that the residue of ethanol and n-propanol in the solid powders were in accordance with the general provisions of the current Chinese Pharmacopoeia. According to the developed procedures and optimized conditions, the recovered IL could be reused in five runs at least. General applicability and greenness assessment for the developed process also proved that it is an ideal method, which has potential in large-scale application. Full article

In the context of promoting sustainable and low-carbon agricultural development, this study investigates the effects of rural labor migration (RLM) on agricultural ecological efficiency from the perspective of green total factor productivity (GTFP). Using panel data from 30 Chinese provinces (autonomous regions, municipalities) over 2011–2022, agricultural GTFP is calculated via the SBM–Global Malmquist–Luenberger (SBM–GML) index. Baseline regressions and the spatial Durbin model (SDM) are employed to examine the impacts of labor migration. The research results show that: (1) Agricultural ecological efficiency exhibits significant spatial clustering, demonstrating “high–high” and “low–low” aggregation patterns. (2) RLM significantly enhances local agricultural ecological efficiency while also generating a positive spatial spillover effect. (3) The effects are heterogeneous: northern regions and highly urbanized areas experience stronger positive impacts, whereas southern regions and less urbanized areas show weaker effects. The findings highlight the pivotal role of RLM in promoting agricultural modernization and provide insights for enhancing regional coordination and ecological efficiency. Full article

Under the national “dual-carbon goal” and the pressing demand for sustainable development, eco-city construction and carbon reduction have become critical issues on China’s urban development agenda, closely aligned with the United Nations Sustainable Development Goals (SDGs). However, most studies focus on regional assessments, lacking national-scale evaluations and spatial heterogeneity analysis of obstacles. This study analyzes 280 Chinese cities using a multi-level evaluation system. Analytic hierarchy process (AHP) and entropy weight methods determine index weights, while the comprehensive evaluation method assesses ecological levels. The obstacle diagnosis model identifies key obstacle factors, and geographically weighted regression (GWR) analyzes spatial heterogeneity, computing carbon intensity to explore relationships with eco-cities development. The findings reveal that (1) the ecological level of Chinese cities exhibits a regional pattern of “high in the east, low in the west”; (2) the primary index-level obstacle factors include total per capita water resources, per capita green space area, college full-time faculty per 10,000 people, the proportion of tertiary industries in gross domestic product (GDP), and college students per 10,000 people; at the element level, the main obstacles are environmental bases, social services, economic potential, and innovative capacity; (3) the GWR model reveals that eastern regions should increase water resources, central regions expand green space, and western and northeastern regions enhance innovative capacity and social services to foster balanced development; and (4) carbon intensity follows a “low in the east, high in the west” pattern, with eco-cities scores significantly negatively correlated with carbon intensity (r = −0.235, p < 0.01). This study provides the first comprehensive national-scale evaluation of eco-cities development, providing reference for the construction of eco-cities. Full article

In the context of global sustainable development, ESG has assumed a pivotal role in evaluating corporate performance. To identify the causal effect of ESG disclosure on firm value, we implement a difference-in-differences (DID) analysis using panel data from A-share listed tourism companies between 2012 and 2020. The study revealed that ESG disclosure has significantly increased tourism corporate value by alleviating financing constraints, reducing financial risks, and attracting green investors. The validity of our conclusion is affirmed through a series of robustness checks, including the parallel trend test, placebo test, bacon decomposition, propensity score matching (PSM), and system generalized method of moments (GMM). Heterogeneity analysis indicates that the positive impact of ESG disclosure on the value of tourism firms is more pronounced in samples with state-owned property nature, a separation of CEO and chairman roles, and low green total factor productivity. Furthermore, this effect is significantly stronger for firms in the accommodation and catering and tourism sightseeing sectors. This study contributes by empirically validating the internal transmission channels through which ESG performance affects firm value in the tourism sector, while also demonstrating the heterogeneous nature of this relationship, thereby providing nuanced evidence for developing differentiated ESG strategies. Full article

This study explores how digitalization by focal firms affects carbon performance across the entire supply chain, advancing the literature by adopting a holistic supply chain perspective rather than a single-firm lens. We further draw on dynamic capability theory to explain the mechanisms through which digitalization enhances supply chain carbon performance. Based on an unbalanced panel dataset of Chinese listed firms from 2008 to 2022, we construct a three-tier supply chain panel linking upstream, focal, and downstream firms. The benchmark regression results show that focal firm digitalization significantly enhances overall supply chain carbon performance. Mechanism analyses identify two critical transmission channels: (1) optimizing supply chain resource allocation efficiency, through improved inventory turnover and strengthened supply chain finance; and (2) enabling collaborative technological upgrading, by enhancing the total factor productivity of upstream and downstream partners. Further heterogeneity analysis reveals that the effect of digitalization on improving carbon performance is more pronounced in regions with stronger environmental regulation and in non-regulated industries. In addition, we analyze the moderating role of the Supply-Chain Pilot-Cities Program. The findings provide practical insights for firm managers globally seeking to leverage digitalization for supply chain decarbonization and for policymakers across jurisdictions aiming to design supportive mechanisms that facilitate digital and green integration. Full article

Scenarios of climate change, extensive land use, soil degradation, the loss of native forest cover due to monoculture expansion, and pasture scarcity pose new challenges to livestock farming worldwide. Associated crops emerge as an alternative to mitigate these factors; however, selecting compatible species that do not generate competition and optimize the attributes of the forage is a necessity. Therefore, this study evaluated the effect of a maize and bean association, and cutting time on the morphological variables, yield, and nutritional composition of forage. A randomized complete block design (RCBD) with a 3A × 3C factorial arrangement and three blocks was used. Factor A (associations) had three levels: INIA-604-Morocho maize monoculture (M), M+PER1003544 chaucha bean association (M+F1), and M+PER1003551 chaucha bean association (M+F2). Factor C (maize cutting stage) had three levels: R2 (blister grain), R3 (milky grain), and R4 (pasty grain). A total of 27 experimental units were established. No silage was made; the nutritional quality was evaluated as the raw material for silage. The treatments modulated key attributes for silage. In R4, the M+F2 association (INIA-604-Morocho + PER1003551) showed a higher percentage of dry matter in the system (32.36%) and better mixture quality due to a lower NDF and ADF (48.22% and 23.29%) and higher digestibility and protein values (62.10% and 9.53%). In addition, dry matter yields increased compared with R2 in M+F1 (134.16%), M+F2 (90.56%), and M (138.48%). Although R3 maximized green forage, R4 offered the best combination of quantity and quality for silage (as raw material), reducing the risk of deterioration and improving forage use efficiency. In general, combining maize with beans and adjusting the cut to R4 optimizes the production and quality of the raw material for silage, with the criterion that these findings pertain to pre-ensiled material and should be validated in future studies. Full article

The adoption of robust fiscal policies is a critical pathway for China to control pollution, promote green development, and advance ecological civilization. This study examines the “Comprehensive Demonstration City of Energy Conservation and Emission Reduction Fiscal Policy” pilot project as a quasi-natural experiment. Employing the Slack-Based Measure Directional Distance Function (SBM-DDF) model and a multi-period Difference-in-Difference (DID) approach, we assess the impact of the energy conservation and emission reduction fiscal policy (ECERFP) on urban green total factor productivity (UGTFP). The results indicate that ECERFP significantly enhances UGTFP. This finding remains robust across multiple tests, including parallel trends, placebo tests, and the Goodman–Bacon decomposition. Mechanism analysis indicates that ECERFP enhances UGTFP mainly through technological innovation and improved energy efficiency. However, its effectiveness varies by geographical location, resource endowment, and city size. While ECERFP can promote urban energy conservation and end-of-pipe pollution control, it also carries the potential risk of inducing a “pollution haven” effect. To maximize the fiscal policy’s leverage and the resource allocation effects, a comprehensive strategy is required—one that advances energy efficiency, stimulates technological innovation, tailors energy conservation measures to local conditions, and nurtures the development of new productive forces to support sustainable urban growth. Full article

Digital literacy (DL) among farmers serves as a vital link between digital technology and green sustainable development, significantly enhancing agricultural green total factor productivity (AGTFP). This study employs panel data from the China Family Panel Studies (CFPS) covering 2014–2020, applying a two-way fixed effects model and machine learning techniques to examine the influence of farmers’ digital literacy on AGTFP. The results indicate that DL positively contributes to AGTFP. Further heterogeneity analysis shows stronger effects among male farmers, households with low trust, and those within the working-age population. Mechanism analysis indicates that social capital accumulation mediates the relationship, whereas agricultural socialization services strengthen the positive impact of DL on AGTFP. Additional analysis using machine learning models reveals that the impact of farmers’ digital literacy on AGTFP changes over time. Specifically, entertainment and learning-oriented network use enhances AGTFP, whereas work-related, social, and lifestyle-related use suppresses it. This study offers a more nuanced understanding by shifting from traditional macro-level frameworks to a micro-level perspective focused on farmers’ digital literacy. Moreover, the innovative application of explainable machine learning provides empirical evidence for the underlying drivers of AGTFP. Full article

This paper evaluates the eco-efficiency of crop production in the European Union (EU) and the Republic of Serbia for the period 2015–2023, using a stochastic frontier analysis (SFA) model based on panel data. Eco-efficiency was assessed as the ratio of agricultural output to key environmental pressures, with expenditures on fertilizers, plant protection products, and energy serving as proxies for ecological burden. The analysis shows that the average eco-efficiency score (Total EE) across the sample is 59.26%, implying that nearly 41% of inputs could be reduced without decreasing output. Decomposition reveals high residual eco-efficiency (93.62%) and lower persistent eco-efficiency (63.30%), suggesting that systematic inefficiencies dominate and are primarily linked to internal farm-level factors such as management practices, organizational structures, and technology adoption. Serbia’s total eco-efficiency score of 63.0% places it close to the EU average, confirming structural similarities with Southern and Eastern European countries. Eco-efficiency scores exhibit notable cross-country variation, ranging from approximately 35% to 96%. About 59% of countries fall within the 50–75% interval, while roughly 11% exceed 75%, indicating considerable scope for further improvement. Cluster analysis further indicates that while Serbia belongs to the lower-intensity group, it has significant potential to converge toward EU frontrunners through farm-level improvements. The findings highlight the importance of targeting internal determinants of efficiency, while recognizing that policy measures can provide enabling conditions and long-term incentives for the green transition. A coherent policy for the green transition should prioritize farm-level structural upgrades, such as technology adoption, advisory and knowledge transfer, and sustainable nutrient and soil management, supported by enabling CAP instruments (eco-schemes and GAEC) and IPARD measures to accelerate improvements in resource efficiency and environmental performance. Full article

Using a balanced provincial panel of 31 Chinese regions (2014–2022), this study examines how Digital Inclusive Finance (DIF) and Rural E-Commerce (RE) jointly shape the urban–rural income gap. Two-way fixed effects and instrumental-variable estimators mitigate confounding. Both DIF and RE are associated with narrower gaps, and the interaction term is negative and robust across specifications. Mechanism evidence indicates that the coupling operates through higher Agricultural Green Total Factor Productivity, expanded rural credit supply, and stronger entrepreneurship. Effects are larger in Central/Western provinces and are most pronounced when DIF’s usage-depth and digital-support components are salient. For policymakers and managers, the findings support bundled investments in digital rails, platform logistics, and e-commerce–linked credit, with priority to lagging regions and programs that deepen usage. Overall, the results provide a tractable systems approach to aligning finance and markets for inclusive rural transformation. Full article

As a critical ecological barrier and key dryland agricultural zone in China, the Loess Plateau is faced with acute tensions between food security risks arising from cropland abandonment (CA) and the imperatives of ecological conservation. Yet, existing research has failed to adequately capture the long-term, high-spatiotemporal-resolution dynamics of abandonment in this region or to quantitatively couple its driving mechanisms with implications for food security. To address these gaps, this study establishes a high-precision identification system for CA tailored to the Plateau’s complex topographic conditions, distinguishing among interannual abandonment, multiyear abandonment, conversion to forest/grassland, and reclamation. Leveraging long-term data from 2000 to 2023 and integrating the Mann–Kendall test with the random forest algorithm, we examine the spatiotemporal trajectories, driving forces, and food security consequences of CA. Guided by a “type differentiation–grade classification–temporal tracking” framework, the analysis reveals a marked transition in dominant drivers from “socioeconomic factors” to “topographic–climatic factors.” It further identifies an “increasing loss–slowing growth” effect of abandonment on grain production, alongside a “pressure alleviation” trend in per capita carrying capacity. The results showed that: (1) Between 2000 and 2023, the area of CA on the Loess Plateau expanded from 2.72 million ha to 6.96 million ha, with high-grade abandonment (≥8 years) accounting for 58.9% of the total and being spatially concentrated in the hilly–gully regions of northern Shaanxi and eastern Gansu; (2) The Grain for Green Project (GFGP) peaked at approximately 340,000 hectares in 2018, followed by a slight decline, but has generally remained at around 300,000 hectares since then; (3) The reclamation rate of CA remained between 5% and 12% during 2003–2015, with minimal overall fluctuations, but after 2016, it gradually increased and peaked at 23.4% in 2022; (4) In terms of driving forces, population density (14.99%) was the primary determinant in 2005, whereas by 2020, slope (15.43%) and mean annual precipitation (15.63%) emerged as core factors; and (5) Grain yield losses attributable to abandonment increased from less than 100 t to nearly 450 t, though the growth rate slowed after 2016, accompanied by gradual alleviation of pressure on per capita carrying capacity. Overall, the study offers robust empirical evidence to inform cropland protection, food security strategies, and sustainable agricultural development policies on the Loess Plateau. Full article

Improving green total factor productivity (GTFP) is critical for balancing economic benefits and ecological constraints. While most existing studies emphasize the pivotal role of governments in GTFP enhancement, they predominantly treat governments as homogeneous entities, overlooking the fundamental premise of local government attention allocation. Analyzing 2010–2020 data from 285 Chinese cities, this study reveals that increased local government green attention significantly stimulates GTFP through three channels: fostering green technology collaboration among firms, deepening green involvement of public research institutions, and elevating green innovation quality. Heterogeneity analyses demonstrate amplified effects in cities characterized by intense intergovernmental competition, stringent intellectual property protection, robust fiscal capacity, and advanced technological infrastructure, but attenuated impacts in resource-dependent regions with heavy reliance on extractive industries. Full article

Golden thistle (Scolymus hispanicus L.) is a wild edible green of high nutritional value, used in the traditional Mediterranean diet. Nowadays, there is an increasing demand from consumers for golden thistle and concomitantly an increasing interest in integrating it into modern cultivation systems. Soilless culture is a promising cultivation option that can maximize yield and quality of golden thistle. The aim of this study was to examine the combined effect of electrical conductivity (EC) and nitrogen (N) supply level on growth and nutritional quality of golden thistle grown on a substrate in a soilless cropping system. The two experimental factors were examined in a 2-factorial experiment with two EC levels, a low (2.2 dS m⁻¹) and high (2.8 dS m⁻¹), combined with two total-N (NO₃⁻ + NH₄⁺) supply levels, low (13.30 mmol L⁻¹) and high (17.30 mmoL L⁻¹), in the supplied nutrient solution. Root fresh and dry weight (commercial yield) were unaffected by treatments; however, high EC significantly reduced shoot fresh and dry biomass by 21 and 28% compared to low EC. High EC increased K⁺ concentrations in shoots and roots but decreased shoot Ca²⁺ level. Nitrate concentration in the drainage solution and plant tissues was primarily driven by N supply, with high N increasing leaf NO₃⁻ by up to 45% without surpassing the regulatory safety limit. Water productivity did not differ among treatments, but low EC improved agronomic efficiency of K⁺, Ca²⁺, Mg²⁺, and S, while low N enhanced N agronomic efficiency by 44%. Overall, low EC promoted vegetative growth and nutrient use efficiency, while increasing N above 13.3 mmol L⁻¹ offered no yield benefit and raised tissue nitrate levels. For optimal yield and quality, a nutrient solution with low EC and N supply is recommended for the soilless cultivation of golden thistle. Full article