Search Results (6,128)

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. Full article

Background/Objectives: Physical restraints and seclusion remain ethically contested interventions in psychiatric care, raising significant concerns regarding patient safety, dignity, and therapeutic impact. Despite growing international momentum towards restraint-reduction strategies, their use persists across the Eastern Mediterranean Region (EMR), an area that has been the subject of limited systematic attention. This review synthesises evidence on the knowledge, attitudes, and experiences of nurses and individuals with mental illness regarding these practices in EMR psychiatric settings. Methods: Following PRISMA 2020 guidelines (PROSPERO: CRD42023383751), we systematically searched nine electronic databases for studies published up to June 2023, supplemented by backward and forward citation searching. Multiple reviewers independently screened records against predefined eligibility criteria, with disagreements resolved through consensus. Methodological quality was assessed using Joanna Briggs Institute (JBI) Critical Appraisal tools, and reporting quality was evaluated using an adapted CROSS checklist; these two appraisal dimensions were conducted and reported independently. Findings were integrated through narrative synthesis. Results: From 4634 identified records, 19 studies conducted across 11 EMR countries met the inclusion criteria. Nursing knowledge deficits were identified across multiple settings, and attitudes towards restraint practices were predominantly negative. Individuals with mental illness consistently described restraint as humiliating, punitive, and physically distressing. Recurrent challenges identified across studies included inadequate staff training, chronic understaffing, and limited access to restraint-reduction alternatives. Conclusions: Substantial gaps in nursing knowledge and training persist across the EMR. The findings of this review, while derived predominantly from cross-sectional studies with convenience samples, suggest that evidence-based education programmes, standardised restraint-reduction policies, and patient-centred care frameworks warrant prioritisation to safeguard the rights, safety, and dignity of individuals with mental illness in this region. Longitudinal and experimental research is needed to confirm these directions and establish their effectiveness within EMR contexts. Full article

Vietnam’s exports expanded dramatically from $14.5 billion in 2000 to $405 billion in 2024, elevating the country to the world’s 22nd largest exporter despite persistent global shocks. This paper introduces the application of the Causal Machine Learning Approach to Resilience Estimation (CLARE) to industry-level trade analysis, utilizing a comprehensive panel of 97 HS2 sectors from 2000 to 2024 (2425 observations) drawn from UN COMTRADE and WITS databases. We implement Double Machine Learning to estimate causal effects of the Global Financial Crisis (2008–2009) and COVID-19 pandemic (2020–2021) on export growth. Results reveal stark industry disparities: electrical machinery (HS85) exhibits exceptional resilience, fueled by 72% high-technology content and low product concentration, while knitted apparel (HS61) proves highly vulnerable. Fixed effect regressions substantiate core hypotheses: a 10-percentage-point increase in high-tech share elevates the resilience index by 0.031 points (approximately 4.1% relative to the sample mean); a one-standard-deviation reduction in product HHI (0.14 units) yields a 0.026-point gain (3.6% relative); and each additional FTA contributes 0.047 points (approximately 6.2% relative), with all estimates significant at conventional levels. Robustness encompassing alternative learners, detrended outcomes, and synthetic controls upholds findings. Policy recommendations center on accelerating high-tech global value chain integration—targeting semiconductors and electric vehicles—while optimizing CPTPP and EVFTA utilization (currently 35%) and mitigating US–China market concentration (45% of exports). These insights chart pathways for Vietnam’s Vision 2045 high-income ambition amid intensifying geopolitical and climate risks, providing a replicable framework for other export-reliant emerging economies. Full article

Objectives: This study examines how different benefit payment modes under China’s long-term care insurance (LTCI) program influence healthcare utilization and medical expenditures among middle-aged and older adults. Specifically, it compares the effects of in-kind benefits and mixed benefits on healthcare service use and financial burden. Methods: This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, focusing on middle-aged and older adults with functional limitations. Exploiting the staggered implementation of LTCI pilot programs across 14 cities, a difference-in-differences (DID) approach is employed to estimate the causal effects of different benefit payment modes on healthcare utilization and expenditures. Heterogeneity analyses are conducted to explore differences between rural and urban populations. Results: The results indicate that the in-kind benefit mode significantly reduces inpatient visits, total medical costs, and out-of-pocket expenditures. By contrast, the mixed benefit mode shows only a modest reduction observed mainly in outpatient visits. Heterogeneity analysis further reveals that in-kind benefits are particularly effective in reducing healthcare utilization and medical expenditures among rural residents, while urban residents experience higher reductions in out-of-pocket spending. Conclusions: These findings highlight the importance of benefit design in shaping the effectiveness of LTCI policies. Prioritizing service-based benefits may improve healthcare system efficiency and reduce financial burdens among older adults. The results provide policy-relevant insights for optimizing LTCI benefit design in China and other aging societies. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

In the context of intensifying climate change, understanding the spatial heterogeneity of urban climate risk is critical to effective climate governance in the United States. This study takes 251 major Metropolitan Statistical Areas (MSAs) in the United States as the analytical unit and establishes a multidimensional urban climate risk assessment framework covering hazard risk, exposure vulnerability, and adaptive capacity. Principal Component Analysis (PCA) is adopted for dimensionality reduction to extract key factors, and K-means clustering is used to identify the spatial differentiation characteristics of climate risk across these MSAs. The results show that climate risk in U.S. MSAs presents significant spatial disparities and can be categorized into four types: high resource and adaptive capacity, high exposure with insufficient adaptive support, complex socio-environmental vulnerability, and low current vulnerability with latent cumulative risk. Based on these findings, this study proposes targeted policy recommendations, including promoting inter-MSA coordination and adaptive capacity spillover, implementing gray–green integrated infrastructure development and enhancing social resilience in the southeastern coastal regions, strengthening equity orientation in climate governance, and advancing proactive governance of cumulative and chronic risks. These conclusions provide a reference for relevant authorities to formulate climate policies. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

To comprehensively investigate the aerosol optical properties and vertical structures over the northeastern Tibetan Plateau (TP), a field campaign was conducted from January to August 2023 in the Hainan Tibetan Autonomous Prefecture. Ground-based sunphotometer measurements yielded a mean aerosol optical depth (AOD) of 0.18 and an Ångström exponent (AE) of 1.20 over the study period. The lowest AE, observed in April alongside the highest aerosol loading, suggests a predominance of dust aerosols during this period. This finding is further supported by the elevated vertical extinction profiles derived from LiDAR measurements, indicating long-range transboundary transport of dust aerosols from northern desert regions. Ground-based AOD measurements were used to validate satellite-derived MODIS retrievals and the assimilated MERRA-2 reanalysis product. Among the aerosol types examined, dust aerosols exhibited the highest accuracy in both AOD and AE validation. MERRA-2 was found to systematically underestimate AOD by 22% and AE by 35%. Nevertheless, due to its tighter expected error envelope, lower overall errors, and superior temporal continuity and spatial coverage, MERRA-2 remains a reliable data source for subsequent analyses. A long-term analysis spanning 2006 to 2025 identifies 2011 as a turning point, after which AOD declined at a rate of 0.0022 per year. This sustained reduction highlights the effectiveness of China’s air pollution prevention and control policies. Collectively, these findings provide essential insights for refining satellite retrieval algorithms and aerosol–climate models over the TP. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

This study examines the relationship between major government support programs and farm-level technical efficiency in Thailand’s sticky rice sector. While existing studies have extensively analyzed rice efficiency, limited attention has been given to distinguishing the efficiency implications of different policy instruments or to modeling dependence between stochastic shocks and inefficiency. Methodologically, we employ a copula-based stochastic frontier efficiency effects model that jointly estimates production and inefficiency determinants while allowing for flexible dependence between noise and inefficiency components. Empirically, we use primary survey data from 429 farmers in Northern Thailand. The results indicate that participation in the debt moratorium program is positively associated with technical efficiency, whereas the widely implemented 1000-baht-per-rai subsidy is negatively associated with efficiency. The cost-reduction program exhibits no statistically significant association. The mean technical efficiency is 0.458, with a distribution concentrated at both low and high efficiency levels, indicating substantial heterogeneity across farmers. Full article

This study examines the impact of digital transformation on enterprise operating costs and elucidates its underlying transmission mechanisms. Digital transformation is measured using a text-based indicator constructed from digital-transformation-related keyword frequencies in firms’ annual reports. Using an unbalanced panel of Chinese A-share listed firms from 2007 to 2023, we employ two-way fixed effects models, mediation analysis, and instrumental-variable estimation for empirical analysis. The findings reveal: (1) Digital transformation significantly reduces enterprise operating costs, with this conclusion maintaining robustness across a comprehensive series of endogeneity treatments and alternative specifications. (2) Enterprise innovation, highly skilled talent, and corporate governance appear to be important channels through which digital transformation contributes to cost reduction. The results are consistent with a complete mediation pattern for enterprise innovation, a partial mediation pattern for highly skilled talent, and a significant mediating role for corporate governance. (3) The cost-reducing effect appears more evident in state-owned enterprises, growth-stage enterprises, and firms located in eastern regions, while the central-region results suggest possible short-term cost increases. This study helps clarify the internal mechanisms through which digital transformation affects enterprise cost control and provides empirical evidence that may inform firms’ digital strategies and related policy design. From a sustainability perspective, these findings suggest that digital transformation may help improve resource efficiency, reduce organizational waste, and strengthen long-term resilience, thereby carrying potential implications for sustainable economic development. Full article

Attention has been drawn internationally to the carbon footprint of the healthcare sector, its impact upon climate change and promises that have been made to reduce carbon emissions. Even so, there are, as yet, not many reports about steps that have been taken in the practical setting to bring about the promised reductions. This review is intended to provide some guidance on actions that could prove beneficial. It includes examples of steps that have been undertaken and shown to be viable options in the practical setting and that now need to be implemented more widely. Certain types of medical textiles contribute more substantially to the carbon footprint of healthcare than others. To achieve significant reductions, attention needs to be focused on reducing the environmental impact of hospital and care centre linen, textile filter components of HVAC systems and PPE, such as gowns, drapes and facemasks, rather than on implantable items and specialist medical devices. Policy makers, those officials responsible for procurement and healthcare practitioners all need to become more involved in ensuring that the correct guidance and resulting actions are implemented in a coordinated fashion. Full article

This study provides an in-depth examination of the association between sustainable agricultural practices and economic sustainability among small-scale farmers in the canton of Ambato, Ecuador, with attention to gender differences in the adoption of specific practices. The study used a cross-sectional quantitative design and structural equation modeling (SEM) based on data from 150 farmers. The results demonstrate that intention to adopt sustainable practices was positively associated with economic sustainability (β = 0.96), and perceived benefits also showed a positive relationship with economic sustainability (β = 0.29). Conversely, agricultural practices showed a negative direct structural coefficient with economic sustainability (β = −0.29), thereby suggesting that the short-term costs of implementation may reduce immediate economic returns in resource-constrained contexts. Descriptive results indicated differences in the adoption of specific practices by sex, especially in crop rotation, biological pest control, and efficient irrigation, although the evidence for gender differences in economic sustainability was mainly descriptive rather than inferential. The model showed acceptable fit (CFI = 0.934, TLI = 0.917, RMSEA = 0.065). Overall, these findings contribute empirical evidence from an Andean agricultural context and suggest that training, technical support, and transition-cost reduction policies are necessary to strengthen the economic viability of sustainable agriculture. Full article

The growing interconnection of critical engineering infrastructure through IoT introduces unprecedented exposure to cyber threats. Emerging quantum computing capabilities pose a transformative risk to classical cryptographic primitives such as Rivest–Shamir–Adleman and Elliptic-Curve Cryptography, which underpin secure communication and device authentication in industrial control systems, power grids, transportation networks, and healthcare infrastructure. This paper investigates quantum-resistant encryption, often termed post-quantum cryptography (PQC), as a sustainable security paradigm for IoT communication within critical systems. By analyzing lattice-based, code-based, multivariate, and hash-based schemes, the study evaluates trade-offs between computational cost, memory footprint, and latency constraints intrinsic to resource-limited IoT nodes. A hybrid architectural framework integrating the National Institute of Standards and Technology-standardized algorithms (e.g., Cryptographic Suite for Algebraic Lattices—Kyber, Dilithium) with lightweight symmetric primitives (e.g., Ascon, GIFT block cipher in Combined Feedback mode) is proposed for secure data transmission across heterogeneous IoT layers. Experimental simulations benchmark key-exchange throughput, ciphertext expansion, and resilience against quantum-adversarial models, demonstrating up to 65% reduction in handshake latency compared to baseline lattice implementations under constrained conditions. The paper concludes with policy and engineering recommendations for the adoption of quantum-resistant IoT protocols in energy, transportation, and industrial automation sectors, highlighting alignment with global PQC migration roadmaps and IEC 62443 cybersecurity standards. Full article