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In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives.

Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.