Search Results (5,323)

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Background: Health equity can only be achieved when every individual has access to quality healthcare without fear of being discriminated against. This study analyzed the sociodemographic characteristics associated with self-reported racial discrimination when receiving medical care in the United States. Methods: This quantitative cross-sectional study utilized the 2022 National Trends Survey 6. We performed a logistic regression analysis using 6102 survey responses from study participants who answered the question about perceived discrimination. Results: Older adults aged 75 years and above had significantly lower odds of reporting perceived discrimination when receiving medical care compared to those aged 18–34 years (AOR = 0.24; 95% CI: 0.10–0.58). The odds of reporting perceived discrimination were significantly higher among non-Hispanic Blacks (AOR = 7.30; 95% CI: 4.48–11.88), Hispanics (AOR = 3.56; 95% CI: 2.45–5.17), non-Hispanic Asians (AOR = 5.95; 95% CI: 2.25–15.73), and individuals identifying as non-Hispanic Other (AOR = 10.91; 95% CI: 5.42–21.98), compared to non-Hispanic Whites. Compared to individuals from households earning less than USD 20,000, the odds of reporting perceived discrimination when receiving medical care were significantly lower among individuals from households earning between USD 50,000 and <USD 75,000 (AOR = 0.42; 95% CI: 0.23–0.78) and those earning USD 75,000 or more (AOR = 0.43; 95% CI: 0.22–0.83). Conclusions: Despite having a multicultural and ethnically diverse population, racial discrimination persists in the United States and has become a barrier to achieving health equity. Health organizations should implement policies that ensure health workers attend mandatory anti-racism training. Full article

The mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

Willow stands (Salix spp.) are an essential part of riparian ecosystems, as they sustain biodiversity and provide bioenergy solutions. The present review synthesizes the global scientific literature about the management of willow stands. In order to achieve this goal, we used a dual approach combining bibliometric analysis with traditional literature review. As such, we consulted 416 publications published between 1978 and 2024. This allowed us to identify key species, ecosystem services, conservation strategies, and management issues. The results we have obtained show a diversity of approaches, with an increase in short-rotation coppice (SRC) systems and the multiple roles covered by willow stands (carbon sequestration, biomass production, riparian restoration, and habitat provision). The key trends we have identified show a shift toward topics such as climate resilience, ecological restoration, and precision forestry. This trend has become especially pronounced over the past decade (2014–2024), as reflected in the increasing use of these keywords in the literature. However, as willow systems expand in scale and function—from biomass production to ecological restoration—they also raise complex challenges, including invasive tendencies in non-native regions and uncertainties surrounding biodiversity impacts and soil carbon dynamics over the long term. The present review is a guide for forest policies and, more specifically, for future research, linking the need to integrate and use adaptive strategies in order to maintain the willow stands. Full article

Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article

The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking. Full article

Soil sealing and land take are increasingly recognised as critical environmental and land use planning challenges across Europe. Although these issues have received limited attention in Baltic policymaking and the academic literature to date, available data indicate ongoing land consumption despite population decline. This study aims to analyse soil sealing patterns in Estonia, Latvia, and Lithuania between 2018 and 2021 using CLC+ Backbone data, linking them to demographic shifts and local planning frameworks. Results reveal that soil sealing increased in nearly all municipalities across the Baltic states, regardless of population trends. The analysis highlights that shrinking municipalities, constrained by limited resources and declining populations, are structurally disadvantaged in terms of land use efficiency, particularly when measured by sealed area per capita. Moreover, this study discusses emerging policy tensions, including the narrowing conceptual gap between land take and soil sealing in the proposed EU Soil Monitoring and Resilience Directive, as well as the risk of overlooking broader land artificialisation. The findings underscore the need for context-sensitive, multi-scalar approaches to land use monitoring and governance, particularly in sparsely populated and demographically imbalanced regions, such as the Baltic states. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Food security is crucial for social stability and economic development. Ensuring food security in the arid region of Northwest China presents unique challenges due to limited water and soil resources. This study addresses these challenges by integrating a comprehensive water and soil resource matching assessment with grain production forecasting. Based on data from 2000 to 2020, this research projects the food security status to 2035 using the GM(1,1) model, incorporating a comprehensive index of soil and water resource matching and regression analysis to inform production forecasts. Key assumptions include continued historical trends in population growth, urbanization, and dietary shifts towards an increased animal protein consumption. The findings revealed a consistent upward trend in grain production from 2000 to 2020, with an average annual growth rate of 3.5%. Corn and wheat emerged as the dominant grain crops. Certain provinces demonstrated comparative advantages for specific crops like rice and wheat. The most significant finding is that despite the projected growth in the total grain output by 2035 compared to 2020, the regional grain self-sufficiency rate is projected to range from 79.6% to 84.1%, falling below critical food security benchmarks set by the FAO and China. This projected shortfall carries significant implications, underscoring a serious challenge to regional food security and highlighting the region’s increasing vulnerability to external food supply fluctuations. The findings strongly signal that current trends are insufficient and necessitate urgent and proactive policy interventions. To address this, practical policy recommendations include promoting water-saving technologies, enhancing regional cooperation, and strategically utilizing the international grain trade to ensure regional food security. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Marine biomass, particularly from waste streams, by-products, underutilized, invasive, or potential cultivable marine species, offers a sustainable source of high-value biopolymers such as collagen and chitin. These macromolecules have gained significant attention due to their biocompatibility, biodegradability, functional versatility, and broad applicability across health, food, wellness, and environmental fields. This review highlights recent advances in the uses of marine-derived collagen and chitin/chitosan. In alignment with the United Nations Sustainable Development Goals (SDGs), we analyze how these applications contribute to sustainability, particularly in SDGs related to responsible consumption and production, good health and well-being, and life below water. Furthermore, we contextualize the advancement of product development using marine collagen and chitin/chitosan within the European Union’s Blue bioeconomy strategies, highlighting trends in scientific research and technological innovation through bibliometric and patent data. Finally, the review addresses challenges facing the development of robust value chains for these marine biopolymers, including collaboration, regulatory hurdles, supply-chain constraints, policy and financial support, education and training, and the need for integrated marine resource management. The paper concludes with recommendations for fostering innovation and sustainability in the valorization of these marine resources. Full article

Recent years have seen frequent heavy rainfall events in Japan, often linked to Baiu fronts and typhoons. These events are exacerbated by global warming, leading to an increased frequency and intensity. As floods represent a serious threat to sustainable urban development and community resilience, this study contributes to sustainability-focused risk reduction through integrated analysis. This study focuses on the 2 June 2023 heavy rain disaster in Toyohashi City, Japan, which caused extensive damage due to flooding from the Yagyu and Umeda Rivers. Using numerical models, this study accurately reproduces flooding patterns, revealing that high tides amplified the inundation area by 1.5 times at the Yagyu River. A resident questionnaire conducted in collaboration with Toyohashi City identifies key trends in evacuation behavior and disaster information usage. Traditional media such as TV remain dominant, but younger generations leverage electronic devices for disaster updates. These insights emphasize the need for targeted information dissemination and enhanced disaster preparedness strategies, including online materials and flexible training programs. The methods and findings presented in this study can inform local and regional governments in building adaptive disaster management policies, which contribute to a more sustainable society. Full article