A Typical Scenario Generation Method Based on KDE-Copula for PV Hosting Capacity Analysis in Distribution Networks

The environmental Kuznets curve for PM2.5 pollution in Beijing-Tianjin-Hebei region of China: A spatial panel data approach

Comparisons of aerosol composition and sources in different cities or regions are rather limited, yet important for an in-depth understanding of the spatial diversity of aerosol pollution in China. In this study, the data originating from 25 different winter aerosol mass spectrometer (AMS)/aerosol chemical speciation monitor (ACSM) studies were used to provide spatial coverage of the Beijing-Tianjin-Hebei (BTH), Guanzhong (GZ), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions. The spatial distribution and diurnal variations in aerosol composition and organic sources were analyzed to investigate the aerosol characteristics in the four regions. It was found that there were differences in the compositions of non-refractory particulate matter across the regions, e.g., more sulfate in the PRD versus more nitrate in the YRD, as well as in the organic sources, e.g., more coal combustion in BTH versus more biomass burning in GZ. The characteristics of the composition of NR-PM are similar when the campaigns were classified according to the winter of different years or the cities of different regions. The diurnal variation of the PRD-sulfate indicated its regional nature, whereas the organics from burning sources in two regions of northern China exhibited local characteristics. Based on these findings, we suggest that strict control policies for coal combustion and biomass burning emissions should be enforced in the BTH and GZ regions, respectively.

Definition and calculation of hierarchical ecological water requirement in areas with substantial human activity—A case study of the Beijing–Tianjin-Hebei region

Rural areas are the basic units of social development and contain residents’ production and living activities, so understanding the nature of rural development has essential theoretical and practical significance. This paper first constructs a rural development evaluation index system, then analyzes the changes in the development level and development pattern of rural areas in Beijing, Tianjin and Hebei from 2009 to 2020 in five aspects: prosperous industry, eco-friendly, civilized countryside, effective governance, and affluent living, and then further explore the changes in their comprehensive rural development level and development pattern. Finally, the main drivers of sustainable development in rural areas and their dynamic impacts are studied and analyzed using the boosted regression trees method. The research results show that: (1) from 2009 to 2020, all five aspects of rural development in the Beijing–Tianjin–Hebei rural areas have progressed to different degrees, among which the development level of affluent living has progressed very significantly and is the primary driver of sustainable rural development in the region; (2) the development level of rural areas in the Beijing–Tianjin–Hebei region has improved in general, but the development speed of major agricultural areas such as Baoding, Xingtai and Handan slower; (3) improving rural medical and health conditions, narrowing the urban–rural gap, and activating farmers’ vitality are the main ways to promote sustainable rural development. The results of the study help to gain an in-depth understanding of the rural development in the Beijing–Tianjin–Hebei region and also have important reference significance for sustainable rural development in other countries or regions.

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Under the strategy of “Digital China” and “Sustainable Development,” the synergistic development of digital economy and green economy has become a crucial topic. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study investigates the direct effect, intrinsic mechanism, and spatial spillover effect of digital technology development (DTD) on urban green development efficiency (GDE). The empirical results show that (1) DTD significantly improves urban GDE in the BTH region, and it passes the endogeneity test, (2) DTD can enhance urban GDE by improving the environmental regulation intensity and technological innovation level in the BTH region; however, the industrial structure optimization weakens the promotion effect of DTD on urban GDE in the BTH region, which shows a “masking effect,” (3) the kernel density estimation method and ArcGIS technology reveal the existence of “digital divide” and GDE differences among cities in the BTH region. Moreover, the spatial distribution pattern of DTD gradually forms “H–H” and “L-L” clusters in the BTH region, and (4) DTD also increases the GDE of neighboring cities through spatial spillover effects in the BTH region, and it passes the robustness test of replacing the spatial weight matrix. This study is important for the BTH region to simultaneously solve economic development and environmental problems in the context of digitalization.

Air pollution episodes during the COVID-19 outbreak in the Beijing–Tianjin–Hebei region of China: An insight into the transport pathways and source distribution

Large amounts of air pollutants emitted from massive coal combustion result in the air quality deterioration and threaten public health in China. To improve air quality, the Chinese government released the coal cap policy to reduce coal consumption. So it is important and necessary to understand the possible environmental impact and relevant health benefits from the coal cap policy. The purpose of this paper is to quantify the air quality improvement and to evaluate the health benefits from the implementation of the coal cap policy, with the Beijing-Tianjin-Hebei (BTH) region as the study area. The results showed that the emissions of SO2, NOx, CO, VOCs, PM10, and PM2.5 could be reduced by 20-40% in the BTH region in 2020 and all pollutants from industrial boilers notably decreased. Under the coal cap policy, the PM2.5 concentration in the whole region would fall by 11.27%, and the total economic benefit from health impacts could achieve 26.61 (13.29 to 39.14) billion RMB (3.9 billion USD) in the BTH region in 2020, accounting for 0.43% (0.21 to 0.63%) of regional GDP in 2013. This study demonstrated the quantification of environmental effect and health benefit from the coal cap policy, which could be used for the complete cost-benefit analysis and provide the sufficient support for policy makers to implement the coal cap policy in the BTH region and other areas of China.

Small and fragmented surface water bodies play critical roles in local hydrology, ecosystem functioning, and climate regulation. However, the prevalent 10-m resolution of current large-scale water products fails to adequately capture these features, limiting their applicability in high-precision monitoring and ecological assessments. To address this gap, we developed a novel method for generating sub-metre surface water maps using freely available Sentinel-1/2 imagery. Our workflow involves four key steps: (1) extracting seven spectral features to enhance the spectral separability of surface water; (2) developing a deep learning-based semantic segmentation framework that integrates super-resolution reconstruction; (3) generating sub-metre reference data via object-based classification of high-resolution Google Earth imagery; and (4) training and applying the model across four representative regions in China. The resulting maps achieved high accuracy (precision: 97.22%–98.22%; recall: 97.82%–98.77%; F1-score: 97.63%–98.50%) and demonstrated a substantially improved detection rate for small water bodies compared to the 10-m ESA WorldCover v100 product and the Automated Water Extraction Index (AWEI). In addition to enhanced spatial fidelity, the proposed method is fully transferable across regions and offers strong potential for temporal analysis, thereby supporting high-resolution, long-term monitoring of surface water dynamics from existing medium-resolution archives. This study presents a practical and adaptable framework for improving the accuracy of surface water mapping by utilizing open-access satellite imagery, with potential for application across diverse geographic contexts and sustainability-driven initiatives.

Background and Objectives: China’s natural forest conservation policy impacts community livelihoods, with farmers’ policy awareness being a key determinant of conservation effectiveness. However, research gaps remain regarding how this policy shapes farmers’ perceptions of livelihood capital drivers and influences their willingness to participate in sustainable forest management. With this study, we aim to quantify the relationship between livelihood capital and farmers’ policy awareness, providing scientific evidence for formulating more effective sustainable policies. Materials and Methods: Using household survey data, we conducted empirical analysis on 498 randomly sampled households from nine villages in Menghai County and Changting County. Generalized ordered logit and binary logistic regression models were employed to examine the impact of multidimensional livelihood capital on policy awareness levels. Results: Our findings indicate heterogeneous effects of livelihood capital. Conclusions: Policymakers should prioritize targeted interventions to enhance the effectiveness of natural forest conservation policies by moving away from a one-size-fits-all approach. Research Highlights and Significance: This study reveals that livelihood capital across different dimensions exerts complex and inconsistent effects on farmers’ policy cognition. Through comparative analysis of two representative regions in China, this study provides empirical evidence for this core finding from both ecological and economic perspectives. The results further offer precise policy implications for enhancing forest conservation policy effectiveness by optimizing livelihood capital structures.

Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China.

This paper presents a feed-forward neural network (NN) for hosting capacity analysis (HCA) in real-time using information from a limited number of buses, addressing the challenge of data scarcity and the need for efficient HCA with minimal input data. A limited number of buses in the distribution network is identified, referred to as pilot buses, using an approach that maximizes observability and controllability while minimizing voltage deviations at load buses to ensure network robustness. A large number of loading and electrical generation scenarios are generated using Monte Carlo simulation to train the NN with voltage information as inputs and HC as output. This trained NN can then predict the hosting capacity for distribution networks within seconds with new inputs, offering a highly efficient and practical solution for distribution utilities. The effectiveness of the proposed approach is demonstrated on the IEEE 123 bus distribution network and a rural feeder in New South Wales, Australia. Results show that the model predicts hosting capacity in 0.04 s for the IEEE 123 network and 0.18 s for the real network, with an average error of less than 1%, showcasing its accuracy and reliability. This rapid and precise prediction capability enhances distribution network management and operational efficiency for utilities. • A feed-forward neural network is proposed for quick hosting capacity analysis. • Only voltage information from pilot buses is used to train the neural network. • The method is tested on the IEEE 123 bus system and a rural feeder in Australia. • The proposed method shows promising results and requires fewer inputs.

Extreme heat events are frequent in the Beijing–Tianjin–Hebei (BTH) region due to global warming and accelerated urbanization. While previous studies have analyzed the trend of extreme heat events in the Beijing–Tianjin–Hebei (BTH) region, the interdecadal changes of these events remain unclear. Therefore, this study aims to analyze the interdecadal temporal and spatial characteristics of summer extreme heat events in the BTH region using daily mean and maximum temperature datasets from 174 stations over the period 1979–2020. The results are shown as follows: (1) From 1979 to 2020, extreme heat events showed an overall upward trend in the BTH region. There were similarities in the changes in the extreme maximum temperature (TXx) and the number of high-temperature days (Htd) between different generations, and both were low until the mid-1990s. (2) In terms of the spatial pattern, TXx and Htd both showed the spatial distribution characteristics of being high in the south and low in the north. Extreme heat events in the BTH region were mainly concentrated in Beijing City, Tianjin City, and the eastern region of Hebei, and the TXx increase in most areas reached 1.5–2.0 °C. (3) The number of high-temperature days (Htd) increased significantly in the background of global warming, especially in Beijing, Tianjin, and Shijiazhuang Cities. (4) Extreme heat events in the BTH region mainly occurred in June and July, and the interdecadal changes showed a decreasing trend in June and an increasing trend in July. A high proportion of Htd was concentrated in Northern Hebei Province in July.

The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing–Tianjin–Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study.

Sources of black carbon during severe haze events in the Beijing–Tianjin–Hebei region using the adjoint method