Search Results (1,630)

Green methane has been deemed as a low CO₂ emission gas. The cost to produce green ethane varies considerably by location and technologies (USD 15/GJ to USD 60/GJ). Although green methane has higher price than the average price of market natural gas in Australia (USD 11–40/GJ between 2019 and 2023), it is currently significantly lower than the production cost for green hydrogen, with the levelized cost of hydrogen (LCOH) at USD 6.6/kg. Green methane production can utilise different processing steps. Separation processes require energy to separate CO₂, with the remaining issue of safely storing the captured CO₂ or venting it to the atmosphere. Direct catalytic biogas methanation (e-methane) does not require the separation of CO₂ but converts CO₂ together with CH₄ to a purer stream of CH₄, converting the CO₂ to an energy product_. E-methane consequently can be considered as an alternative energy carrier to store off-peak electricity from the grid, commonly called power-to-gas technology (P2G). Furthermore, injecting green methane into gas pipelines does not require significant gas infrastructure upgrading and has no upper limit, as it is compatible with natural gas. Here we review the status of biogas and direct green methane production from biogas around the world and assess technologies that are used to produce green methane via separation or direct catalytic conversion. We evaluate their techno-economic assessment results, with a particular focus on e-methane, identifying the opportunity as a pathway to supply low-emission gas with the perspective of a future e-methane industry within Australia. Full article

Environmental protection tax (EPT), as a major tool to improve air quality and reduce carbon emissions, is of great significance for promoting urban low-carbon transformation. In this context, this paper has compiled a dataset from 282 Chinese cities during 2006–2022 and empirically identify the implication of EPT for carbon emissions at the city level by using the intensity difference-in-differences (I-DID) model. The result discloses that EPT greatly lowers carbon emissions by an average of 10.9% compared to non-pilot cities. Even after conducting some robustness checks, the result remains unchanged. Mechanism testing reveals that EPT curbs carbon emissions through enhancing energy utilization efficiency, fostering green technological advancements, and modernizing urban industries. Meanwhile, we show that EPT exerts a more substantial effect on carbon emissions in innovative cities, central and western cities, non-industrial-based cities, and non-resource-dependent cities. More importantly, EPT greatly promotes imitation and learning in neighboring regions, forming a radiation impact upon carbon reduction in surrounding areas. Hence, these results offer an important decision-making guide for optimizing the EPT system, strengthening the coordinated governance of carbon emission across regions, and ultimately promoting urban low-carbon development. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

The purpose of this study is to systematically examine the impact of technological innovation on the coordinated development of regional economies and its internal mechanism. It is aimed at revealing whether and how technological innovation promotes the coordinated development of regional economies, and further identifying its heterogeneity characteristics and boundary conditions in the space–time dimension. The research was conducted using panel data for 258 prefecture-level cities in China from 2011 to 2021. This study found that technological innovation significantly promoted the coordinated development of regional economies; this effect was more prominent in China’s eastern region and the Yangtze River Economic Belt. The mechanism test shows that technological innovation can optimize regional resource allocation and narrow the development gap by promoting industrial structure upgrades and rationalization. Further analysis shows that the level of marketization has a nonlinear regulatory effect on the coordination effect of technological innovation, with two threshold levels. A heterogeneity analysis reveals significant differences in the effects of technological innovation in different regions in China, especially in the western region and the northwest side of the Hu Changyong line. The research leads to four key policy recommendations. First, it is important to strengthen the core driving role of technological innovation and implement regionally differentiated innovation support policies. Second, industrial structure upgrades should be encouraged through industrial chain coordination. The third recommendation is to improve the market-oriented institutional environment and minimize barriers to factor flow. Fourth, supporting coordinated policies, such as optimizing human capital and introducing high-quality foreign capital, is necessary to establish a sustainable long-term mechanism for regional coordinated development. Full article

While the corporate adoption of artificial intelligence (AI) is accelerating, its environmental consequences remain insufficiently understood, particularly in absolute firm-level energy consumption. The main objective of this study is to empirically determine the causal impact of AI adoption on absolute firm-level energy consumption in Chinese publicly listed companies, with a particular focus on the mediating role of green innovation and the moderating role of digital capabilities. This study provides the first large-scale micro-level evidence on how AI adoption shapes corporate energy use, drawing on panel data from Chinese non-financial listed firms during 2011–2022. We construct a novel AI adoption index via Word2Vec-based textual analysis of annual reports and estimate its impact using firm fixed effects, instrumental variables, mediation models, and multiple robustness checks. Results show that AI adoption significantly reduces total energy consumption, with a 1% increase in AI intensity associated with an estimated 0.48% decrease in energy use. Green innovation emerges as a key mediating channel, while the energy-saving benefits are amplified in firms with advanced digital transformation and IT-oriented executive teams. Heterogeneity analyses indicate more substantial effects among large firms, private enterprises, non-energy-intensive sectors, and firms in digitally lagging regions, suggesting capability-driven and context-dependent dynamics. This study advances the literature on digital transformation and corporate sustainability by uncovering the mechanisms and boundary conditions of AI’s environmental impact and offers actionable insights for aligning AI investments with carbon reduction targets and industrial upgrading in emerging economies. Full article

To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article

Against the backdrop of a complex global economic landscape, foreign trade serves as a critical link integrating China’s marine economy with the global market, playing an indispensable role in advancing high-quality marine economic development in China and realizing the strategic goal of building a strong maritime nation. Utilizing panel data covering 11 coastal provinces and municipalities in China from 2013 to 2022, this research adopts a double machine learning approach to examine the effects and mechanisms through which the high-quality development of foreign trade (HQD) shapes high-quality marine economic development (THQ) in China. The empirical results demonstrate that (1) high-quality development of foreign trade significantly promotes high-quality marine economic development in China, with a 1-unit increase in the former corresponding to a 1.437-unit rise in the latter. This finding withstands multiple robustness checks. (2) Mechanism analysis indicates that this promotion occurs through three channels: strengthening marine environmental regulation, enhancing marine labor productivity, and upgrading the marine industrial structure. (3) Heterogeneity analysis shows that the effect of high-quality foreign trade is stronger in China’s eastern marine economic region. Simultaneously, the trade development environment emerges as a key factor exerting a significantly positive influence on marine economic quality during China’s foreign trade advancement. The empirical findings propose the following optimization countermeasures for high-quality marine economic development: strengthening marine environmental regulation, enhancing marine labor productivity, and promoting the upgrading of the marine industrial structure. Full article

As a key component of new infrastructure, smart logistics is becoming an essential driver for reducing foreign trade costs and risks and promoting the sustainable development of foreign trade. Using panel data from 286 prefecture level and above cities from 2014 to 2023, this article attempts to refine the measurement of smart logistics level from provincial to municipal levels, construct a two-way fixed effect model and a mediation effect model, and deeply explore the inherent relationship between smart logistics, industrial structure upgrading, and sustainable development of foreign trade. The results reveal that: (1) smart logistics significantly promotes the sustainable development of foreign trade. (2) Rationalization and advancement of industrial structure play an intermediary role between the two. (3) Market integration has a positive moderating effect on the path of “smart logistics—industrial structure rationalization”, but the moderating effect is not significant in other paths. It has been confirmed that there is a “siphon effect” in the advantageous regions. (4) Heterogeneity analysis shows that the effect of smart logistics on foreign trade promotion is more significant in the central and inland regions. This study provides a theoretical basis and practical inspiration for optimizing regional smart logistics layout and deepening industrial structure adjustment. Full article

This study examines the impact of public data openness on environmental welfare performance using a quasi-natural experimental approach based on the establishment of prefecture-level city public data openness platforms. Our findings reveal that public data openness significantly improves urban environmental welfare performance. Furthermore, heterogeneity analysis highlights that public data openness can play a more positive role in cities in eastern China, cities with greater fiscal autonomy, and cities where local governments place greater emphasis on environmental protection. Mechanism analysis demonstrates that public data openness enhances environmental welfare performance through stricter environmental regulatory constraints, industrial structure upgrading, increased public participation and supervision, and advancements in innovation and entrepreneurship. Extensive analysis shows that public data openness within a spatial framework can significantly enhance environmental welfare performance in the region. However, this process will generate a triple “siphon effect” that inhibits improvements in urban environmental welfare performance in surrounding areas. Additionally, this effect exhibits a certain degree of geographical attenuation influenced by economic interdependence, with an attenuation boundary of 1000 km. This study injects internet and big data thinking into ecological civilization construction, endowing it with new models, new scenarios, and new momentum, and providing a brand-new approach to sustainable development practices. Full article

Given the demand for sustainable food solutions in China and the underutilization of bovine by-products, this study aimed to optimize the marinating process of bovine liver, heart, and rumen while evaluating their storage stability. An orthogonal experimental design was employed to systematically optimize the marinating agent ratio and incorporate natural antioxidants to inhibit lipid oxidation and microbial spoilage. Results demonstrated that the optimized marinating formula, which included 0.3 g/kg rosemary extract, exhibited optimal antioxidant and antimicrobial effects. This strategy not only slowed product pH decline but also improved product yield and texture, and significantly reduced thiobarbituric acid reactive substances (TBARS) values and carbonyl content (p < 0.05), while maintaining favorable sensory scores and extending shelf life. The study indicates that targeted marinating technology holds potential for transforming bovine by-products into high-value-added food products, offering innovative solutions to address both economic and environmental challenges and establishing a technical foundation for efficient by-product utilization and industrial upgrading. Full article

Amid the health-conscious consumption trend, functional foods rich in γ-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, a traditional grain naturally abundant in these nutrients, faces quality assessment challenges due to the time-consuming and destructive nature of conventional methods, hindering large-scale screening. This study pioneers the systematic application of hyperspectral imaging (HSI) for nondestructive detection of GABA and vitamin B9 in millet. Utilizing spectral data from 190 samples across 19 varieties, we developed an innovative “coarse-fine” feature wavelength selection strategy. First, interval-based algorithms (iRF, iVISSA) screened highly correlated wavelength subsets. Second, model population analysis (MPA) algorithms (CARS, BOSS) identified optimal core wavelengths, boosting model efficiency and robustness. Based on this, a stacked BiLSTM–Adaboost model was built, integrating bidirectional long short-term memory networks for sequence dependency and adaptive boosting for enhanced generalization. This enables efficient, rapid, nondestructive, and precise nutrient detection. This interdisciplinary breakthrough establishes a novel pathway for millet nutritional assessment, deepens fundamental research, and provides core support for industrial upgrading, breeding, quality control, and functional food development, supporting national health. Full article

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies. Full article

The current green building assessment system suffers from issues such as insufficient coverage of smart indicators, significant biases in subjective weighting, and weak dynamic adaptability, which restrict the scientific promotion of green buildings. This study focuses on the gaps in the quantitative assessment of smart technologies in China’s green building evaluation standards (such as the current Green Building Evaluation Standard). While domestic standards are relatively well-established in traditional dimensions like energy conservation and environmental protection, there are fragmentation issues in the assessment of smart technologies such as the Internet of Things (IoT) and BIM. Moreover, the coverage of smart indicators in non-civilian building fields is significantly lower than that of international systems such as LEED and BREEAM. This study determined the basic framework of the evaluation indicator system through the Delphi method. Drawing on international experience and contextualized within China’s (GB/T 50378-2019) standards, it systematically integrated secondary indicators including “smart security,” “smart energy,” “smart design,” and “smart services,” and constructed dual-drive evaluation dimensions of “greenization + smartization.” This elevated the proportion of the smartization dimension to 35%, filling the gap in domestic standards regarding the quantitative assessment of smart technologies. In terms of research methods, combined weighting using the Analytic Hierarchy Process (AHP) and entropy weight method was adopted to balance subjective and objective weights and reduce biases (the resource conservation dimension accounted for 39.14% of the combined weights, the highest proportion). By integrating the grey clustering model with the whitening weight function to handle fuzzy information, evaluations were categorized into four grey levels (D/C/B/A), enhancing the dynamic adaptability of the system. Case verification showed that Project A achieved a comprehensive evaluation score of 5.223, with a grade of B. Among its indicators, smart-related ones such as “smart energy” (37.17%) and “smart design” (37.93%) scored significantly higher than traditional indicators, verifying that the system successfully captured the project’s high performance in smart indicators. The research results indicate that the efficient utilization of resources is the core goal of green buildings. Especially under pressures of energy shortages and carbon emissions, energy conservation and resource recycling have become key priorities. The evaluation system constructed in this study can provide theoretical guidance and technical support for the promotion, industrial upgrading, and sustainable development of green buildings (including non-civilian buildings) under the dual-carbon goals. Its characteristic of “dynamic monitoring + smart integration” forms differentiated complementarity with international standards, making it more aligned with the needs of China’s intelligent transformation of buildings. Full article

The inspection of the appearance quality of tea leaves is vital for market classification and value assessment within the tea industry. Nevertheless, many existing detection approaches rely on sophisticated model architectures, which hinder their practical use on devices with limited computational resources. This study proposes a lightweight object detection network, TeaAppearanceLiteNet, tailored for tea leaf appearance analysis. A novel C3k2_PartialConv module is introduced to significantly reduce computational redundancy while maintaining effective feature extraction. The CBMA_MSCA attention mechanism is incorporated to enable the multi-scale modeling of channel attention, enhancing the perception accuracy of features at various scales. By incorporating the Detect_PinwheelShapedConv head, the spatial representation power of the network is significantly improved. In addition, the MPDIoU_ShapeIoU loss is formulated to enhance the correspondence between predicted and ground-truth bounding boxes across multiple dimensions—covering spatial location, geometric shape, and scale—which contributes to a more stable regression and higher detection accuracy. Experimental results demonstrate that, compared to baseline methods, TeaAppearanceLiteNet achieves a 12.27% improvement in accuracy, reaching a mAP@0.5 of 84.06% with an inference speed of 157.81 FPS. The parameter count is only 1.83% of traditional models. The compact and high-efficiency design of TeaAppearanceLiteNet enables its deployment on mobile and edge devices, thereby supporting the digitalization and intelligent upgrading of the tea industry under the framework of smart agriculture. Full article

As the world’s largest manufacturing country, China’s industrial carbon emission reduction is crucial to achieving its “dual carbon” goals. This paper takes Wuxi, a national low-carbon pilot city in Jiangsu Province, as a case, using a bottom-up factor decomposition model to study industrial carbon peak prediction and sector-specific emission reduction strategies. Results show that under the usual-growth scenario (UG), Wuxi’s industrial emissions keep growing and will not peak before 2030, reaching 122.18 million tCO₂ that year. Under the emission-controlled scenario (EC), with industrial structure optimization and energy intensity control, emissions peak in 2026 at 100.55 million tCO₂, 17.7% lower than the baseline. The reinforced-mitigation scenario (RM), combining in-depth structural adjustment and technological upgrade, sees the peak in 2025 at 94.22 million tCO₂, a 22.9% reduction. It is necessary to implement differentiated emission reduction strategies, focusing on high-emission and low-carbon productivity industries such as electricity and heat production, and ferrous metal smelting and rolling. Through precise management and control, the overall emission reduction efficiency can be improved, providing a reference paradigm for the low-carbon transformation of similar industrial cities. Full article