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Transportation infrastructure has an enormous impact on sustainable development. To identify multiple impacts of transportation infrastructure and show emerging trends and challenges, this paper presents a scientometric review based on 2543 published articles from 2000 to 2017 through co-author, co-occurring and co-citation analysis. In addition, the hierarchy of key concepts was analyzed to show emerging research objects, methods and levels according to the clustering information, which includes title, keyword and abstract. The results expressed by visual graphs compared high-impact authors, collaborative relationships among institutions in developed and developing countries. In addition, representative research issues related to the economy, society and environment were identified such as cost overrun, spatial economy, prioritizing structure, local development and land value. Additionally, two future directions, integrated research of various effects and structure analysis of transportation network, are recommended. The findings of this study provide researchers and practitioners with an in-depth understanding of transportation infrastructure’s impacts on sustainable development by visual expression.

Pinus massoniana Lamb. is one of the main timber tree species. There is a large artificial planting area in South China, and this tree has important economic and ecological value. In this research, we built a comprehensive habitat suitability model based on 115 current data and 22 environmental variables to analyze the potential suitable habitat distribution of this species. Future climate change scenarios were defined as four shared socioeconomic pathways (SSPs): SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, SSP 5–8.5) and four periods (including 2021–2040, 2041–2060, 2061–2080, and 2081–2100) based on nine global circulation model datasets. To fully consider the potential distribution of P. massoniana under specific climate change conditions and soil conditions, we constructed an ensemble model using four commonly used model algorithms. The results indicated that the current suitable habitat for P. massoniana covers approximately 1.10 × 106 km2 in southeastern China. In the future, the model results showed that under different climate change scenarios and at different times, the change in suitable habitat for P. massoniana varied; moreover, under moderate climate change scenarios, the average temperature decreased by less than 3 °C and the suitable habitat area decreased slightly, with an area larger than 0.95 × 106 km2. However, under intense warming scenarios, for which the average temperature increased above 3 °C, the suitable habitat for P. massoniana decreased. In the most severe warming scenario, the suitable habitat area for P. massoniana was reduced to 44% of the base climate conditions with severe habitat fragmentation, which should be fully considered in future planting initiatives and plant protection.

The spatiotemporal distribution of phthalate acid esters (phthalates, PAEs) in wastewater treatment plants (WWTPs) in China was studied. The concentration of PAEs in influent and effluent increased from 2009 to 2016, indicating that the exposure level of PAEs in China increased continuously. Although the concentration of PAEs in sewage sludge in China ranged from 33.3 to 298 ug/g, there was no obvious spatial distribution pattern. Among the 19 PAE homologues, DEHP, DnBP, and DIBP were the most abundant phthalates detected in wastewater and sludge. Ecological risk assessment confirmed that there was a high chronic and acute risk of DIBP in effluent since 2015. Therefore, this study highlights the need for further studies on the exposure and toxicology of DIBP. Dietary intake accounted for more than 98% of the total risk, indicating that the risk of sludge application in agricultural land was much higher than that in nonagricultural land. The results from this study will provide valuable information for the safe disposal of sludge and wastewater.

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.

De-carbonization of the transport sector is an important pathway to climate-change mitigation and presents the potential for future lower emissions. To assess the potential quantitatively under different optimization measures, this paper presents a hybrid model combining an integrated machine learning model with the scenario analysis. We compare the training accuracy of the back-propagation neural networks (BPNN), Gaussian process regression (GPR), and support vector machine (SVM) fitting model with different training datasets. The results indicate that the performance of the SVM model is superior to other methods. And the particle swarm optimization (PSO) algorithm is then used to optimize hyper-parameters of the SVM model. Two scenarios including business as usual (BAU) and best case (BC) are set according to the current trends and target trends of driving factors identified by the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. Finally, to find the de-carbonization potentials in the transport sector, the PSO-SVM model is applied to predict transport emissions from 2015 to 2030 under two scenarios. Results show that transport emissions reduce by about 131.36 million tons during 2015–2020 and 372.86 million tons during 2021–2025 in the BC scenario. The findings can effectively track, test, and predict the achievement of policy goals and provide practical guidance for de-carbonization development.

Although machine learning (ML) techniques are increasingly popular in water resource studies, they are not extensively utilized in modeling snowmelt. In this study, we developed a model based on a deep learning long short-term memory (LSTM) for snowmelt-driven discharge modeling in a Himalayan basin. For comparison, we developed the nonlinear autoregressive exogenous model (NARX), Gaussian process regression (GPR), and support vector regression (SVR) models. The snow area derived from moderate resolution imaging spectroradiometer (MODIS) snow images along with remotely sensed meteorological products were utilized as inputs to the models. The Gamma test was conducted to determine the appropriate input combination for the models. The shallow LSTM model with a hidden layer achieved superior results than the deeper LSTM models with multiple hidden layers. Out of seven optimizers tested, Adamax proved to be the aptest optimizer for this study. The evaluation of the ML models was done by the coefficient of determination (R2), mean absolute error (MAE), modified Kling–Gupta efficiency (KGE’), Nash–Sutcliffe efficiency (NSE), and root-mean-squared error (RMSE). The LSTM model (KGE’ = 0.99) enriched with snow cover input achieved the best results followed by NARX (KGE’ = 0.974), GPR (KGE’ = 0.95), and SVR (KGE’ = 0.949), respectively. The outcome of this study proves the applicability of the ML models, especially the LSTM model, in predicting snowmelt driven discharge in the data-scant mountainous watersheds.

BackgroundChina faces the dual challenge of pollution control and carbon reduction amid rapid urbanization and industrialization, while traditional environmental policies struggle to meet the demands of cross-regional coordinated governance.MethodsUsing the Outline of YREB as a policy context, this study systematically evaluates the co-benefits and mechanisms of cross-regional coordination policies on pollution and carbon reduction. Based on panel data from 259 Chinese prefecture-level cities (2014–2019), we employ a coupling coordination model and a difference-in-differences approach to assess policy effectiveness.ResultsThe findings reveal that: (1) Cross-regional coordination policies significantly enhance pollution-carbon synergy in YREB cities through structural integration effects, with the impact strengthening over time and remaining robust across tests; (2) The policy facilitates long-term pollution-carbon synergy governance through three key pathways—industrial green transition (structural), clean energy system co-construction (technological), and cross-regional low-carbon technology diffusion (knowledge-based)—driving a shift in environmental governance from policy-driven external enforcement to development-driven endogenous demand.ConclusionThis study highlights that cross-regional coordination is not only a tool for spatial economic integration but also a structural driver of sustainable environmental governance, providing a novel policy pathway for China’s dual-carbon goals and contributing to global climate governance.

As a result of the COVID-19 pandemic, whether and when the world can reach herd immunity and return to normal life and a strategy for accelerating vaccination programmes constitute major concerns. We employed Metropolis–Hastings sampling and an epidemic model to design experiments based on the current vaccinations administered and a more equitable vaccine allocation scenario. The results show that most high-income countries can reach herd immunity in less than 1 year, whereas low-income countries should reach this state after more than 3 years. With a more equitable vaccine allocation strategy, global herd immunity can be reached in 2021. However, the spread of SARS-CoV-2 variants means that an additional 83 days will be needed to reach global herd immunity and that the number of cumulative cases will increase by 113.37% in 2021. With the more equitable vaccine allocation scenario, the number of cumulative cases will increase by only 5.70% without additional vaccine doses. As SARS-CoV-2 variants arise, herd immunity could be delayed to the point that a return to normal life is theoretically impossible in 2021. Nevertheless, a more equitable global vaccine allocation strategy, such as providing rapid vaccine assistance to low-income countries/regions, can improve the prevention of COVID-19 infection even though the virus could mutate.

Prefabricated construction (PC) is considered to be a solution for construction unsustainability. Promotion of PC is based on measuring the influencing factors for the diffusion of prefabricated construction technology innovation (PCTI). Therefore, this study explored the influencing factors and underpinned mechanism for the diffusion of PCTI from the perspective of construction enterprises’ adoption, with two dimensions of internal and external. A conceptual model with nine hypotheses was first established. Then, this study conducted a questionnaire survey of 119 construction enterprises across the PC industry, and made an empirical analysis using structural equation model (SEM), to test the model. The results found that both construction enterprises and governments play significant roles in the diffusion of PCTI while agencies and consumers do not. Besides, network power of construction enterprises plays a mediating role in the relationship. This study provides decision-making assistances for different stakeholders in the PC industry.

Emergency science communication is an important emergency activity that can enhance public safety awareness and risk perception ability, optimize health-protective behaviors, and reduce losses from emergencies. Certainly, emergency science communication needs to be multisource information and cross-organization. Moreover, increasing cooperation in emergency science communication is the key to improving the effectiveness of science communication in public health emergencies. To clarify the cooperation relationships among emergency organizations in emergency science communication, emergency science communication cooperation networks (ESCCNs) are constructed on the basis of the social network analysis method, and the practice of emergency science communication in response to COVID-19 in China is taken as the case. Through an interpretation of the characteristics of ESCCNs in the emergency response phase and the ongoing emergency phase, the differences in the cooperation modes of public health emergencies in different phases are analyzed. Moreover, the influence of different emergency phases on the formation of the ESCCN of the whole phase is discussed. With the evolution of the emergency phase, the network tightness, equilibrium and connectivity of the ESCCN all tend to increase, whereas network agglomeration decreases. In the emergency response phase, the core-edge features of the ESCCN are obvious, and emergency science communication organizations (ESCOs) are more inclined to form emergency science communication cooperation with other ESCOs of the same type. However, in the ongoing emergency phase, the cooperation relationships between ESCOs are more balanced, and more diversified cross-group cooperation relationships are formed. The diversification of ESCO types, the closeness of ESCO relationships and the connectivity of ESCCNs are the main factors that promote the formation of the ESCCN of the whole phase. Furthermore, implications for strengthening the efficiency of science popularization cooperation in public health emergencies are proposed in connection to matching dynamic characteristics, optimizing resource allocation and strengthening institutional guarantees.