Motor vehicles are a major source of NO₂ emissions, making traffic-related pollution a key target for urban air pollution control management. However, research on traffic-related NO₂ exposure risks in China remains nascent, particularly regarding spatio-temporal variations and exposure inequities. To support evidence-based public health policies, it is essential to investigate group disparities in exposure across both spatial and temporal dimensions. This study utilizes the CALPUFF model and mobile phone signal data to examine the spatio-temporal patterns and population group disparities in NO₂ exposure within Baoshan District, Shanghai, China. The findings reveal a bimodal diurnal pattern, with higher NO₂ exposure levels on weekdays and lower levels on weekends. Areas with heavy traffic and high population density, such as port zones and the outer ring expressway, are identified as the most vulnerable. Furthermore, males and younger age groups experience greater exposure to traffic-related NO₂, whereas elderly individuals are comparatively less exposed.

To address global climate change, developed countries have committed at the 29th session of the Conference of the Parties (COP29) to provide $300 billion annually in climate finance by 2035 to support mitigation and adaptation actions in developing countries. However, the effectiveness of this target remains unclear. This paper, based on the Policy Analysis of Greenhouse Effect - Ice, Climate, and Economics (PAGE-ICE) model, introduces a climate finance module to evaluate the implementation of new climate finance targets under different collection and dispensation principles. It further explores the impact of regional governance capacity and the classification of donor and recipient countries on global climate and economic outcomes. The results show that if climate finance lasts until 2035, the global temperature rise will decrease by 0.13 °C to 0.14 °C in 2050. Extending the financing period to 2050 will further reduce the temperature rise by approximately 0.02 °C and increase the economic benefits of each country as a percentage of GDP by 0.09% to 1.25%. Regarding the dispensation principles, the Carbon Intensity-Adaptation Need (CIAN) principle results in the greatest emission reductions, while the Carbon Reduction Contribution-Adaptation Need (CRCAN) principle facilitates a more equitable distribution of economic benefits among recipient countries. In terms of collection principles, the Responsibility-Capacity Integrated (RCI) principle is a more acceptable burden-sharing option for donor countries compared to other existing principles. Furthermore, weaker governance capacity reduces the effectiveness of climate finance. While shifting China from a recipient to a donor may alleviate pressure on traditional donor countries, it could weaken the overall mitigation effectiveness.

Amidst the backdrop of global climate warming and China’s proactive chase of its carbon peak and carbon neutrality goals, the Huaihe River Basin (HRB), a region of significant strategic importance in the heartland and eastern expanse of the nation is confronted with formidable challenges, including high energy consumption and severe environmental pollution. Despite its substantial contributions to economic development, the traditional development model of the HRB conflicts with the principles of green development, necessitating the urgent exploration of innovative pathways to sustainable progress. Through a comprehensive review of scholarly literature and rigorous theoretical analysis, this study demonstrates that artificial intelligence (AI) can significantly drive green development by enhancing eco-innovation and optimizing industrial structures. Using a panel dataset from 27 cities in the Huaihe River Ecological Economic Belt (HEB) from 2010 to 2022, this study employs a bidirectional fixed-effects model to analyze the repercussions of AI on green development. The baseline regression results show that for every one-unit increase in AI development level (AIDL), HEB’s urban green development level significantly increases by 0.087. This positive influence is further confirmed through robustness tests. We found that AI can indirectly influence the mechanism and pathway of green development through intermediate variables. AI drives green development indirectly through two pathways: green technology innovation and the rationalization of the industrial structure, with a total explanatory power of 56.7% (R2 = 0.812). Based on these findings, we propose vigorously promoting the green effects of AI, refining industrial structures, and leveraging mediating effects to foster sustainable regional development. These insights offer novel perspectives for the green development of the HRB but also provide valuable references for the green transformation of other areas with similar challenges.

With rapid urbanization and increasing mobility demand, urban traffic systems face intensifying congestion, resulting in elevated CO₂ emissions. This paper provides a systematic review of the current status of models estimating CO₂ emissions from urban road traffic, considering their applicability across various traffic management scenarios. Urban road traffic CO₂ emission models can generally be categorized into two main types. Traditional models typically estimate emissions based on average speed, traffic conditions, or vehicle operation modes, whereas data-driven models leverage techniques such as machine learning and deep learning to capture complex emission patterns. The review proposes a set of model selection criteria, namely data availability, computational complexity, interpretability, and transferability. Based on a comparative evaluation of these criteria, the study finds that there is no one-size-fits-all model so far. Instead, model suitability depends heavily on local data infrastructure and specific application needs. Therefore, future work needs to enhance model localization and personalization to improve estimation accuracy, while the integration of spatiotemporal data-driven modeling approaches is likely to become a research hotspot in upcoming studies.

Environmental footprint (EF) as a critical tool for assessing the environmental impacts of human activities has been widely applied in the sustainable development field. Building upon a review of the current research landscape, this study employs bibliometric analysis to identify the intellectual base of EF research through co-citation networks and to outline research status. Following the release of the European Commission EF framework, the research primarily focused on the development and standardization of the product and organization EF methods, and then expanded to global environmental governance frameworks, such as the circular economy and planetary boundaries, promoting multi-scale environmental impact assessment tools. The enhancement of databases and the increasing emphasis on uncertainty analysis in Life Cycle Assessment (LCA) and Multi-regional Input-output models have enhanced the comparability of assessments. EF research has expanded into sectors such as food systems, healthcare, information and communication technology, pharmaceuticals, batteries, and plastics, offering both theoretical and empirical support for green transitions and environmental performance optimization across sectors. Using metals, healthcare, and construction as cases, this study highlights the shared features and distinct characteristics of EF application across sectors. In the metals sector, research addresses both primary extraction and recycling, with inconsistent treatment of uncertainty. Healthcare studies focus mainly on devices and consumables, with limited attention to hospitals, departments, and treatment pathways. In construction, studies cover materials, structures, firms, and technologies, mostly using LCA, but often lack systematic uncertainty analysis. Future direction could further integrate EF with the planetary boundaries framework and circular economy strategies, improve dynamic modeling in methodological robustness, and broaden application to emerging fields such as hydrogen energy, cryptocurrency mining, cloud computing, and digital infrastructure.

With the global push toward carbon neutrality, reducing greenhouse gas emissions in the transportation sector has become increasingly urgent. Electric buses represent a promising solution; however, their full life cycle carbon footprint remains underexplored. This study aims to address this gap by quantifying and comparing the life cycle carbon emissions of pure electric buses. A life cycle assessment (LCA) approach is applied to evaluate emissions across the production, usage, and recycling stages. Scenario analyses are conducted to assess the impact of carbon fiber reinforced polymer (CFRP) as a material substitute, variations in electricity generation mix, coal consumption rates, and the extent of recycled material utilization. Results show that buses using nickel manganese cobalt (NMC) battery type C have the lowest life cycle emissions at 55,814.89 kg CO₂-eq, while those with lithium iron phosphate (LFP) battery type A have the highest, reaching 59,364.10 kg CO₂-eq. During the production stage, the primary emission sources are the body, chassis, battery system, and electricity consumption. Substituting steel and aluminum with CFRP increases production emissions by up to 108.6%. However, in the operational phase, CFRP significantly reduces bus weight by 41.99% and cuts operational carbon emissions by 36.49%. In the recycling stage, NMC battery type C yields the highest emission reduction, achieving 14,943.86 kg CO₂-eq, mainly due to the recovery of nickel and lithium compounds. These findings offer valuable insights for optimizing material choices, energy structures, and recycling strategies to support the low-carbon development of electric buses.

The arid zone of northwestern China is a critical ecological functional area, where terrestrial ecosystems are extremely fragile and difficult to restore once degraded, with significant implications for the regional carbon balance. Urban green spaces play an essential role in carbon sequestration and air purification, making them important carbon sink carriers in arid environments. Taking Lanzhou City as a case study, this research analyzes the carbon sink capacity of cropland, woodland, grassland, wetland, park green space, and other urban green land types from 2000 to 2020, and explores the main factors influencing their evolution. The findings indicate that: (1) Lanzhou's green space was primarily composed of cropland and grassland. However, due to urbanization and the city’s unique valley-type topography, construction land expanded while cropland and grassland areas declined; (2) The total carbon sink decreased from 361,000 tons in 2000 to 354,100 tons in 2020. Nevertheless, the economic value of carbon sinks continued to increase due to the annual rise in carbon prices. Cropland remained the largest contributor to urban green space carbon sinks; and (3) In 2020, land use intensity had a negative effect on carbon sinks, with an impact significantly greater than that of industrial structure or economic development. This study provides a scientific basis for improving urban green space management and enhancing carbon sink capacity in arid urban regions, thereby supporting regional ecological security and sustainable development.

Urban planning has long relied on traditional zoning documents that primarily designate development rights based on land use types. The indirect environmental impacts of development are typically assessed through separate Environmental Impact Assessment (EIA) commissions, which lack legal authority, while official zoning plans carry binding legal power. This division creates a disconnect between the impacts identified in EIAs (intended to be avoided) and the impacts facilitated by zoning plans (designed to be achieved). The legal authority of zoning plans often outweighs the procedural influence of EIAs. In this study, we explore whether exchanging the thematic roles of zoning and EIAs could yield better results. To investigate this, we conceptualize a transformative approach by reassigning the traditional designations in an existing zoning plan to focus instead on climate impact-oriented categories and apply this approach to a real-world case study. Using a Critical Realist analytical framework, we compare the potential impacts of this climate-centric zoning concept with those of traditional zoning. Our findings reveal extensive societal linkages embedded in what appear to be purely technical zoning decisions. We demonstrate how reimagined zoning, informed by contemporary climate impact knowledge, can drive significant systemic change in urban development - surpassing the influence of isolated strategies, policy guidelines, or EIAs, and addressing environmental effects that often fall outside the scope of conventional EIA processes.

In protected agriculture, irrigation methods and input structures play a critical role in shaping carbon and nitrogen footprints as well as ecological-economic performance. Using a life cycle assessment (LCA) approach, this study systematically evaluated the carbon footprint (CF), nitrogen footprint (NF), environmental damage cost (EDC), and net ecosystem economic benefit (NEEB) under conventional surface irrigation (CK) and three negative-pressure irrigation (NPI) treatments (N1, N2, N3). Compared with CK, NPI significantly reduced CF and NF by up to 74.09% and 76.45%, respectively, primarily due to reduced N₂O emissions and fertilizer inputs. NPI also alleviated soil organic carbon loss by 78.85%, underscoring its strong potential for environmental sustainability. However, the high cost of ceramic emitters (accounting for 88.36%~89.80% of Cost_{agricultural input}) and their substantial upstream emissions resulted in a significantly lower net economic benefit (NEB) for NPI compared with CK. Despite this drawback, NPI treatments demonstrated superior ecological-economic efficiency: EDC was reduced by over 85%, and the N1 treatment achieved a NEEB of -10.37 CNY ha^-1, nearly reaching break-even. These results highlight a clear trade-off between environmental benefits and economic feasibility. Policy support (e.g., subsidies, carbon credits) and innovations in emitter materials (e.g., low-carbon or biodegradable alternatives) are essential to improve the overall sustainability and scalability of NPI systems.