Substantial transition to clean household energy mix in rural China

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition

Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China

Abstract High-resolution, dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management. Mobile phone big data have real-time collection, wide coverage, and high resolution advantages and can thus be used to characterize human activities and population distributions at fine spatiotemporal scales. Based on six days of mobile phone user-location signal (MPLS) data, we assessed the dynamic spatiotemporal distribution of the population of Xining City, Qinghai Province, China. The results show that strong temporal regularity exists in the daily activities of local residents. The spatiotemporal distribution of the local population showed a significant downtown-suburban attenuation pattern. Factors such as land use types, holidays, and seasons significantly affect the spatiotemporal patterns of the local population. By combining other spatiotemporal trajectory data, high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions

Assessment Progress and Indicator Analysis on the Implementation of SDG 14 in China

The marine sustainable development goal (SDG 14), as an important part of the 2030 United Nations Sustainable Development Goal system (SDGs), provides a comprehensive framework for solving problems related to marine environment and socio-economic development at the national level. Since 2016, the specific targets and indicators of SDG 14 have been constantly developing and changing. China faces both opportunities and challenges in implementing the specific indicators of SDG 14. On the one hand, there are realistic gaps and limitations in evaluating the progress of China using the UN SDG 14 indicator framework; on the other hand, the voluntary national assessment indicator framework of SDG 14 in China needs to be further improved. Finally, this study suggests that the evaluation index and method system of SDG 14 in China should be improved as soon as possible in order to improve the comprehensive marine management system and capacity. In addition, it is also very important to promote the coordinated and balanced development of multiple objectives and indicators related to marine sustainable development

Formation of Multilayers by Star Polyelectrolytes: Effect of Number of Arms on Chain Interpenetration

We have investigated the influence of number of arms on chain interpenetration in the growth of star poly­[2-(dimethylamino)­ethyl methacrylate] (PDEM)/star poly­(acrylic acid) (PAA) multilayers using a quartz crystal microbalance with dissipation (QCM-D). The oscillations in the changes of dissipation and frequency reflect the chain interpenetration and the variation of the mass of multilayer, respectively. The QCM-D results demonstrate that the growth of multilayers has two different mechanisms in terms of chain interpenetration. That is, the arm chains of star PDEM insert into a predeposited PAA layer to form a swollen multilayer, but the complex of star PAA with predeposited star PDEM is an “octopus-like” structure forming a dense multilayer. The transition between these two penetration modes is controlled by the number of arms in the star polyelectrolytes. As the number of arms of either PAA or PDEM increases, it becomes more difficult for star PDEM to penetrate into the PAA layer, but star PAA can more easily penetrate into the PDEM layer. According to atomic force microscopy and water contact angle measurements, all eight-bilayer multilayer surfaces have similar roughness values, and the surface wettability of the multilayers is dominated by the outermost PDEM layer

Comparison of Socioeconomic Factors between Surrounding and Non-Surrounding Areas of the Qinghai–Tibet Railway before and after Its Construction

As the world’s highest railway, and the longest highland railway, the Qinghai–Tibet Railway (QTR) has been paid considerable attention by researchers. However, most attention has been paid to the ecological and environmental issues affecting it, and sustainable ecological, social, and economic development-related studies of the QTR are rare. In this study, by analyzing the passenger traffic, freight traffic, passenger-kilometers, and freight-kilometers of the QTR for the period 1982–2013 and the transport structure of the Tibetan Plateau (TP) for 1990–2013, the evolutionary process of the transport system in the TP following the construction of the QTR has been revealed. Subsequently, by comparing Gross Domestic Product (GDP), population, industrial structure, and urbanization level at the county and 1 km scales between surrounding and non-surrounding areas of the QTR, the differences in socioeconomic performance before and after its construction were detected. The results show that (1) in the TP, the highway-dominated transport system will break up and an integrated and sustainable transport system will form; (2) at the county scale, the annual growth rates of GDP of counties surrounding the QTR were greater than those of non-surrounding counties for the period 2000–2010. At the 1 km scale, following the opening of the completed line, the GDP of surrounding areas had a greater growth rate than before; (3) analysis at the county and 1 km scales indicated that population was not aggregated into the surrounding areas of the QTR in the period 2000–2010; (4) in terms of industrial structure, the proportion of primary industry decreased continuously, while the proportion of secondary and tertiary industries increased overall in the period 1984–2012. The QTR had no obvious impact on changes in the urbanization level of its surrounding areas