Emissions of human-use antibiotics in China pose a significant threat to aquatic ecosystems and accelerate the spread of antimicrobial resistance. Existing assessments lack the facility-level resolution required for effective policy-making and targeted mitigation. This study aims to enhance the understanding of the spatial distribution of human-use antibiotic emissions to water systems, pathways, risks, and associated mitigation strategies in China. We developed SEAAL-China to quantify emissions of 19 major antibiotics from sewered and unsewered populations across more than 10,000 wastewater treatment plants (WWTPs). The national analysis of the removal efficiencies showed significant differences among treatment technologies, ranging from approximately 30-90%. Additionally, over half (57%) of China's WWTPs employ moderately effective technologies (removal rate <60%). We modeled a total of 3741 t of human-use antibiotic emissions to water systems in 2020, with 41% of the unsewered population accounting for more than half of the total. This highlights the need to expand basic sanitation infrastructure in rural and peri-urban areas to address the sanitation deficit. The emission hotspots are distributed in Guangdong, Shandong, and Henan, primarily driven by factors such as regional GDP and the imbalance in healthcare infrastructure. These areas cover 12% of the country's area and account for more than half of the calculated antibiotic emissions, suggesting that management should prioritize upgrading treatment technologies in hotspot areas to close the efficacy gap. A further risk assessment of antibiotic resistance selection revealed that fluoroquinolones are the dominant class with co-occurring emission and risk hotspots (630 dual-hotspot counties, 21.8% of the national total). Collectively, the above findings may help to inform both region-specific and compound-specific management strategies for human-use antibiotic pollution.

Across many African countries, malnutrition and water stress coexist, yet the water implications of shifting toward healthier diets remain poorly understood. This study evaluates theoretically feasible pathways to reduce the blue water footprint (WF) of the food supply while simultaneously achieving health and water security goals across 42 African countries. Two theoretically feasible scenarios were developed using a constrained analytical framework: Meeting Planetary Health Dietary guidelines (MPHD) and Improving Blue Water Efficiency through strategic trade (IBWE). The results show that the current food supply diverges substantially from health targets, with blue WF of 175.4 m3/capita/year. A total of 43.9% of total virtual water inflow is traded unsustainably. Both modeled dietary scenarios closed the dietary gap toward healthier dietary patterns, resulting in reductions of blue WF by 32% under MPHD and 63.7% under IBWE. This reduction would be achieved through a dietary shift complemented by the restructuring of food imports between domestic production and international trade, especially in water-stressed regions. The study highlights the importance of integrating dietary change, trade structure, and water constraints when designing healthier and more water-efficient food systems in Africa.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), as a pivotal region in China's efforts to combat climate change and improve air quality, holds strategic importance for achieving the nation's dual goals of pollution and carbon mitigation through the synergistic control of greenhouse gases (GHGs) and air pollutants (APs). While its highly open economy and complex supply chains optimize resource allocation, they also complicate the attribution of responsibility and the coordinated governance of these emissions. Therefore, we established an analytical framework for synergistic GHGs-APs control in urban agglomerations using methods such as input-output modelling and structural path analysis. We clarified the major emission sources and key demand-driven factors for embodied emissions in the GBA, revealed critical synergistic pathways, and explored the synergy between GHGs and APs. Findings reveal that developed cities' manufacturing (e.g., electrical equipment and machinery, textiles) and service industries drove more GHGs-APs particularly in Hong Kong. Regionally, these emissions originated mainly from resource-intensive provinces such as Shandong, Hebei, and Henan, while at the city level, flows from Foshan and Guangzhou to Hong Kong dominated, contributing 64 % of intercity embodied emissions. Notably, critical supply chain pathways simultaneously transferred more CO2 and APs, while supply chain length constitutes a key determinant affecting synergy GHGs-APs. Furthermore, major Pearl River Delta cities, Macao and their core industries-particularly automotive manufacturing, electronics, and petrochemicals-demonstrate significant GHGs-APs synergistic effects. These findings offer a scientific basis for differentiated synergistic strategies and coordinated multi-regional, multi-sectoral governance across the urban agglomeration's supply chain.

Understanding the spatial determinants of transport-related carbon emissions is critical for advancing climate-responsive urban development. This study examines how built-environment characteristics, transport infrastructure, and transport mode interact to influence transport carbon emissions across different urban development levels in Taiwan. A typology-sensitive design classified 352 cities and towns into high-, medium-, and low-development areas (HDAs, MDAs, and LDAs, respectively) using the population size, population density, and location quotient (LQ) to capture the scale, intensity, and functional specialization, respectively. Subsequently, separate multiple regression models evaluated tier-contingent associations between transport emissions and indicators of spatial form, infrastructure, and mobility. The results showed that private vehicle usage as the strongest positive correlate of transport carbon emissions across all tiers. However, built-environment effects varied markedly by development tier. In HDAs, emissions were positively associated with building-patch aggregation but negatively with dispersion, suggesting that an excessively concentrated structure may intensify congestion-related inefficiencies while a more distributed spatial structure can moderate traffic pressure. Bus service coverage also effectively reduced emissions in HDAs. In contrast, in MDAs, dispersion was positively associated with emissions, consistent with increased travel demand arising from dispersion-led expansion. In LDAs, both aggregation and dispersion were positively associated with emissions while bus service coverage provided a key mitigating pathway, reflecting essential accessibility where private travel alternatives are limited. This study highlights the value of development-sensitive analyses for improving the interpretability and policy relevance of transport-emissions modelling. Moreover, it provides evidence to inform more effective, equitable, and scalable low-carbon urban policies.

It is necessary to assess the latest temporal trend of trade-embodied employment for formulating timely policies to alleviate the global employment gap. Although the key role of international trade on global employment has been confirmed, there is a lack of research on the benefit of trade to create employment. Based on the multi-regional input–output model, we investigate global employment driven by consumption and international trade in 2012–2022. Furthermore, we construct export-obtained employment benefit (EEB) coefficient and import-promoted employment benefit (IEB) coefficient to quantify the benefit of exports to obtain employment and imports to promote employment, respectively. We found that developing economies presented as significant exporters of trade-embodied employment. Developed economies maintained the main importers of trade-embodied employment, while the role of China became increasingly significant. For the benefit of trade to create employment, Rest of Africa, India, and Rest of Asia and Pacific presented EEB higher than 3, exhibiting high benefit of exports to obtain employment, while the EEB of developed economies were lower than 1. As primary importers of trade-embodied employment, the IEB of the EU and China were higher than 1, showing high benefit of imports to promote employment, while the IEB of the US was lower than 1. This study can provide timely insights for policy making to promote global employment.

A global shift towards healthy and sustainable diets is vital to prevent non-communicable diseases and reduce food systems' environmental impacts. Africa's delicate environment and intricate nutritional challenges call for a thoughtful and sustainable approach to dietary change. This study introduced the Planetary Healthy Diet Index (PHDI) to assess dietary compliance in 37 African countries across five regions, analyzing environmental footprints such as GHG emissions, blue water footprint, and cropland use tied to the EAT-Lancet reference diet. Findings reveal poor adherence to the planetary healthy diet, with significant deviations in key food components. Improved adherence could reduce GHG emissions but it might increase water and cropland use, with regional variations. Optimal dietary shifts involve reducing red meat, tubers and sugars while increasing vegetables, fruits, plant proteins, and dairy. With few African countries having dietary guidelines, the EAT-Lancet diet provides a foundational for improving health and environmental sustainability with additional policies.

Quantification and monitoring of urban fossil fuel CO2 (FFCO2) emissions with sufficient accuracy and spatial granularity are critical to emission control and climate change mitigation. We use a top-down Bayesian inversion method to constrain FFCO2 emissions from the Xiamen-Zhangzhou-Quanzhou metropolitan area, China, based on high-resolution areal snapshots of total column CO2 (XCO2) from OCO-3 snapshot area maps (SAMs) from September 2019 to July 2023. Based on five available overpasses, the observed XCO2 enhancements range from 0.70 +/- 0.53 ppm to 2.29 +/- 1.16 ppm. Inversions are conducted to disentangle mixed imprints and jointly constrain emissions from area sources in Xiamen, local power plants in Xiamen, and other adjacent urban sources. An overall improvement in the fossil fuel XCO2 enhancement is shown with the RMSE reduced by 24 % and the correlation coefficient improved by 65 %. While, sectoral and overpass scale variations of performance are revealed, with the observational representativity, amount of XCO2 samples, and spatial displacements between modeled and observed XCO2 plumes being primary limitation factors. Based on inversion results with sufficient robustness (on 26 December 2019 and 12 august 2022), the mean constrained emission of Xiamen is 1.68 x 10(4) tCd(-1), close to bottom-up emission accounting based on local statistics and facility-level fuel consumptions, with the relative differences <18 %. Given that only two OCO-3 SAMs overpasses produce robust inversions over nearly four years, the results demonstrate more challenges than potential of using XCO2 mapping observations to constrain sources with spatial granularity. Possible pathways for further improvements are discussed.

Air temperature is a crucial climatic indicator that significantly impacts various sectors, including the environment, hydrology, agriculture, and disaster management. Accurate and timely air temperature forecasting is essential for effective risk management and future planning. This study investigates the performance of Deep Learning (DL) models for nowcasting air temperature in various regions of Pakistan. We utilize hourly temperature data (2018–2023) from four meteorological sites (Murree, Swat, Multan, and Sukkur), representing different climate conditions. The models compared in this study include Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Feed Forward Neural Networks (FNN), and a hybrid CNN-LSTM architecture. The performance of these models is evaluated using several statistical criteria (MSE, MAE, RMSE, and MAPE) and visual comparisons. The results indicate that while the LSTM model performs best, the CNN, FNN, and hybrid CNN-LSTM models also show considerable promise. However, it can be concluded that the LSTM model outperforms other models. These findings highlight the adaptability of DL algorithms in predicting temperature across various climatic scenarios. The implications of this study are significant for sectors such as agriculture, transportation, and disaster relief, which depend on accurate temperature forecasts for effective resource allocation and climate risk management. By advancing nowcasting technologies in Pakistan, this research contributes to enhancing resilience to weather-related challenges.

Urban roof development serve as a form of urban tinkering that could provide favorable conditions to meet wicked food, water, and energy challenges. In this research, three urban roof systems are designed featuring food production, rainwater collection, building energy saving, and photovoltaic power generation, which are named the bare roof system, green roof system, and open-air farming roof system. Here we establish a generalizable framework to reveal the multifaceted tradeoffs of urban roof schemes integrating geographic information system, life cycle assessment, and multi-objective optimization and decision-making. Testing the framework in a typical compact city Shenzhen, China, results show that the rooftop suitability for food-water-energy function development within the city ranges from 26.21 % to 42.83 %. When implementing a city-wide roof system upscaling, the harvested rainwater on rooftops would contribute to 82.5 % of urban recycled water utilization target in 2035 and exhibit the fewest economic costs, but comes at the cost of life cycle carbon emissions and land occupation with 1850 kt and 140 km2. City-wide roof farming scenario shows potential to achieve the local selfsufficiency of vegetables, and stands out as the avoided transboundary energy footprints which are 4.5 times greater than life cycle energy consumption. Our research can foster the multiple tradeoff understanding between upscaling roof systems and urban food-water-energy nexus. The findings will assist in multi-objective decisionmaking for roof planning and sustainable transition in cities.

Decarbonisation increases the complexity of the spatio-temporal dynamics in CO2 emission distributions within the power system. Existing power system planning studies have not thoroughly explored the spatio-temporal allocation of emission responsibility, consequently leading to inefficiencies for certain stakeholders. This research innovatively integrates the hourly quasi-input-output (QIO) approach into the multi-city capacity expansion planning (CEP) model, thus enabling an allocation of carbon emissions with high spatio-temporal resolutions. Using Anhui Province, China, with its 16 cities as a case study, the model is validated and reveals that the traditional annual emission accounting approach is projected to either underestimate or overestimate CO2 emissions in 2040, as hourly emission factors could vary sixfold due to day-night variations in photovoltaic power output. Emission factors tend to be underestimated in cities dominated by renewables, while coaldominant cities face the opposite issue due to the substantial transmission of high-embodied-emission electricity to other cities at nighttime. To mitigate the unfair allocation of emission burdens among cities, renewabledominant cities need to take greater responsibility for managing CO2 emissions in coal-dominant cities, particularly in future power systems with higher renewable energy penetration.

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

The use of tailings sand for CO2 mineralization and utilization (CMU) has been emerging globally, offering a sustainable solution for repurposing tailings sand while establishing a stable carbon sink. Despite several reports that evaluated process performance and project benefits, existing research has not delved into the project layout focusing on the tailings pond facility and the feasibility considerations from a cluster and ownership perspective. Conducting facility-scale refined mineralized carbon sink (MCS) and layout feasibility assessment is a key issue that needs to be addressed urgently. Here, we accounted for CMU potential (CMUP) of tailings sand using a sample of 6219 tailings pond ownerships in China, predicted future annual carbon sinks through an innovative ARIMA-cloud model, and analyzed the feasibility of project layouts within potential clusters concerning enterprise size. Our results indicate that the CMUP of accumulated tailings sand in China amounts to 1817.87 Mt. In a combined scenario involving future technological leapfrogging and increased utilization, the average annual carbon sink by 2030 is projected to be 24.73 Mt. A considerable reduction in project layout feasibility across all scenarios was observed due to the mismatch between carbon clusters and enterprise size. Our study reveals that CMU using tailings sand should be regarded as one of the essential green technologies in the portfolio of strategic Chinese CO2 mitigation. However, we highlight the necessity of aligning cluster programs with the support capacity of the ownership enterprise to ensure the optimal project success.

The projected rise in atmospheric CO2 levels to 550 ppm by mid-century may reduce protein, iron, and zinc levels in certain cereal crops by 3–17 %. In China, staple foods provide nearly 50 % of total energy and 40 % of essential nutrients, and their cultivation exacerbates environmental stress; adjusting staple food consumption may bring environmental and health benefits. Using China Health and Nutrition Survey (CHNS) data, this study compared current staple food consumption (SBAU) to an optimized balanced diet scenario with the consumption of whole grains and legumes replaces excessive refined grains. Findings reveal that SBAU falls short of recommended nutrient intake (RNI), while the optimized scenario offsets the nutritional impact of the elevated CO2, exceeding 95 % of the RNI for zinc and iron from staple food. Additionally, transition to the optimized scenario also reduces greenhouse gas emissions and blue water consumption by 7 % and 39 %, respectively.

The pollution reduction and carbon-cutting measures of Guangdong-Hong Kong-Macao Greater Bay Area (GBA) set a benchmark for sustainable development in China's urban agglomerations. While the automobile manufacturing industry significantly boosts the GBA's economic growth, its extensive cross-regional and cross-sector supply chains pose challenges for CO2 and pollution emission control. Analyzing emissions characteristics and transfer paths can clarify the GBA’s role and guide targeted reduction measures. According to the multi-region input-output (MRIO) model and structural path analysis (SPA), this study clarifies that the automobile manufacturing industry in Guangdong, Hong Kong and Macao causes more than 72% of the CO2 and air pollutant (SO2, NOX, PM10) spillover to other regions, where the energy-resource-dominant upstream regions of Hebei, Shandong, and Henan emit high levels of CO2 and air pollutants, according for 19% of indirect supply chain emissions; the energy sector, non-metals sector, and metals sector are the key upstream, according for 59% of indirect supply chain emissions. Furthermore, our study identifies the Beijing-Tianjin-Hebei and neighboring regions, parts of the western region, and the GBA region and the metal products industry, automobile manufacturing industry, and water, electricity, and gas supply industry sector as the core area and key sector with significant synergistic effects on CO2 and air pollutant emissions. Therefore, implementing emission reduction measures in the identified core areas and sectors will contribute to the achievement of synergistic enhancement. These findings can inform decision-makers to promote sustainable development in the region. Furthermore, the research perspective based on the industrial chain offers new insights into defining regional responsibility for CO2 emissions and enhancing regional cooperation.

China faces a dual challenge of improving air quality and reducing greenhouse gas (GHG) emissions. Stringent clean air actions gradually narrow the end-of-pipe (EOP) pollution control potential. Meanwhile, pursuing carbon peaking will reduce air pollution and health risks. However, the impact on air quality and health gains in individual Chinese provinces has not been assessed with a specific focus on local policies. Here, typical shared socio-economic pathways (SSPs) and local policies (i.e., business as usual, BAU; end-of-pipe controls, EOP; co-control mitigation, CCM) are combined to set three scenarios (i.e., BAU-SSP3, EOP-SSP4, CCM-SSP1). Under these three scenarios, we couple the Low Emissions Analysis Platform (LEAP) model, an air quality model and health risk assessment methodology to evaluate the characteristics of carbon peaking in Fujian Province. PM2.5 air quality and impacts on public health are assessed, using the metric of the deaths attributable to PM2.5 pollution (DAPP). The results show that energy-related CO2 emissions will only peak before 2030 in the CCM-SSP1 scenario. In this context, air pollutant emission pathways reveal that mitigation is limited under the EOP-SSP4 scenario, necessitating further mitigation under the CCM-SSP1 scenario. The annual average PM2.5 level is projected to be 16.5 μg·m-3 in 2035 with a corresponding decrease in DAPP of 297 (95 % confidence intervals: 217-308) compared with that of 2020. Despite the significant improvements in PM2.5 air quality and health gains under the CCM-SSP1 scenario, reaching the 5 μg·m-3 target of the World Health Organization (WHO) remains difficult. Furthermore, population aging will require stronger PM2.5 mitigation to enhance health gains. This study provides a valuable reference for other developing regions to co-control air pollution and GHGs.

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters.

In the context of ambitious greenhouse gas (GHG) mitigation strategies in emerging regions, addressing the prevailing uncertainty and its key influential factors is crucial. While uncertainty analysis of GHG mitigation pathways has been extensively explored, the systematic identification and quantification of pivotal factors has been notably absent. This study introduces a novel methodology that combines Quasi-Monte Carlo simulation with Sobol variance decomposition, which identifies key influential factors and traces the flow of uncertainties. Applied in Anhui Province, a rapidly developing region in China, our findings indicate an uncertain GHG emission proportion of 6.2 % by 2030, escalating to 68.6 % by 2060, with a 95 % confidence interval. This uncertainty results in a cumulative projection error of 2.1 billion tons of CO(2)e from 2020 to 2070. The per capita GDP factor emerges as the predominant influence, alongside the increasing impact of renewable energy factor and UHV import electricity factor. In our uncertainty flow analysis, the energy transformation sector is identified as the principal contributor to total uncertainty, driven significantly by economic and energy-related factors.

Expressway slopes within the boundary of expressway construction land have the potential to be transformed from conventional land use to valuable utilization through the installation of photovoltaic (PV) systems. Considering the continuous increase in expressway mileage, the PV potential could be enormous; therefore, a method to quantify such potential is urgently needed. This study proposes a potential assessment method for expressway slope photovoltaics. First, the suitable locations of expressway slopes for installing PV systems are identified on the basis of publicly available digital maps, combined with the principles of PV site selection and geographic information systems. Then, the PVsyst model is used to simulate the power generation efficiency and power output of the PV systems and to evaluate their PV potential, contribution, and economic benefits. The implementation of the proposed method in Fujian Province, China, indicates that the installed capacity of PV systems could reach 16.3 GW, with an annual power output of 17,940 GWh, contributing to the peak shaving of power grids and regional energy supply. The reductions in CO2, SOx, and NOx emissions are 16.65 Mt, 8970 t, and 3947 t per year, respectively. Considering carbon trading, the payback period is shortened to 11.7 years, with the return on investment of 65.38 % and the net present value of 52.6 billion RMB over a 25-year operational period. Additionally, this project could create 19.88 % of jobs in the energy sector in Fujian Province, providing 27,861 jobs during the construction phase and 18,248 jobs during the operation and maintenance phase. Thus, this project is economically viable and generates positive energy, environmental, and social benefits, contributing to the promotion of local energy transition and sustainable development.

Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.