Mitigating heat-related mortality risk in Shanghai, China: system dynamics modeling simulations

Numerous studies in epidemiology, meteorology, and climate change research have demonstrated a significant association between abnormal ambient temperature and mortality. However, there is a shortage of research attention to a systematic assessment of potential mitigation measures which could effectively reduce the heat-related morbidity and mortality risks. This study first illustrates a conceptualization of a systems analysis version of urban framework for climate service (UFCS). It then constructs a system dynamics (SD) model for the UFCS and employs this model to quantify the impacts of heat waves on public health system in Shanghai and to evaluate the performances of two mitigation measures in the context of a real heat wave event in July 2013 in the city. Simulation results show that in comparison with the baseline without mitigation measures, if the hospital system could prepare 20% of beds available for emergency response to heat waves once receiving the warning in advance, the number of daily deaths could be reduced by 40–60 (15.8–19.5%) on the 2 days of day 7 and day 8; if increasing the minimum living allowance of 790 RMB/month in 2013 by 20%, the number of daily deaths could be reduced by 50–70 (17.7–21.9%) on the 2 days of day 8 and day 12. This tool can help policy makers systematically evaluate adaptation and mitigation options based on performance assessment, thus strengthening urban resilience to changing climate

Heat wave, electricity rationing, and trade-offs between environmental gains and economic losses: The example of Shanghai

In recent decades, many megacities in the world have suffered from increasingly frequent heat waves. During heat waves, air-conditioners, refrigerators, and electric fans add a considerable peak demand on electrical utility grids, and on the supply side, high temperatures exert adverse effects on electricity generation, transmission, and distribution. Without proactive planning and mitigation measures, the overloading would result in more frequent blackouts (the complete failure of electricity distribution) and brownouts (voltage reductions). To facilitate a pro-active planning, which aims to replace blackouts and brownouts by a rationing regime in selected sectors, this research proposes an integrated modeling tool which couples a regression model between daily electricity use and maximum temperature over the summer and a mixed input–output model with supply constraints. With the help of available data in Shanghai, China, we show that this tool is capable of quantitatively estimating the overall economic effects and sequential changes in carbon emissions, which a given magnitude of power rationing in a specific sector can exert across all sectors. The availability of such information would enable decision makers to plan an electricity rationing regime at the sector level to meet the double criterions of minimizing the overall economic losses and maximizing the extent of carbon emission reduction

Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale and a case study of Southwest China

Cropland abandonment has emerged as a prevalent phenomenon in the mountainous areas of China.While there is a general understanding that this new trend is driven by the rising opportunity cost of rural labor, rigorous theoretical and empirical analyses are largely absent. This paper first develops a theoretical model to investigate household decisions on farming scale when off-farm labor market is accessible and there is heterogeneity of farmland productivity and distribution. The model is capable of explaining the hidden reasons of cropland abandonment in sloping and agriculturally less-favored locations. The model also unveils the impacts of heterogeneity of household labor on fallow decisions and the efficiency loss due to an imperfect labor market. The model is empirically tested by applying the Probit and Logit estimators to a unique household and land-plot survey dataset which contains 5258 plots of599 rural households in Chongqing, a provincial level municipality, in Southwest China. The survey shows that more than 30% of the sample plots have been abandoned, mainly since 1992. The econometric results are consistent with our theoretical expectations. This work would help policy-makers and stakeholders to identify areas with a high probability of land abandonment and farming practice which is less sustainable in the mountainous areas

The impacts of increased heat stress events on wheat yield under climate change in China

China is the largest wheat producing country in the world. Wheat is one of the two major staple cereals consumed in the country and about 60% of Chinese population eats the grain daily. To safeguard the production of this important crop, about 85% of wheat areas in the country are under irrigation or high rainfall conditions. However, wheat production in the future will be challenged by the increasing occurrence and magnitude of adverse and extreme weather events. In this paper, we present an analysis that combines outputs from a wide range of General Circulation Models (GCMs) with observational data to produce more detailed projections of local climate suitable for assessing the impact of increasing heat stress events on wheat yield. We run the assessment at 36 representative sites in China using the crop growth model CSM-CropSim Wheat of DSSAT 4.5. The simulations based on historical data show that this model is suitable for quantifying yield damages caused by heat stress. In comparison with the observations of baseline 1996-2005, our simulations for the future indicate that by 2100, the projected increases in heat stress would lead to an ensemble-mean yield reduction of –7.1% (with a probability of 80%) and –17.5% (with a probability of 96%) for winter wheat and spring wheat, respectively, under the irrigated condition. Although such losses can be fully compensated by CO2 fertilization effect as parameterized in DSSAT 4.5, a great caution is needed in interpreting this fertilization effect because existing crop dynamic models are unable to incorporate the effect of CO2 acclimation (the growth enhancing effect decreases over time) and other offsetting forces

Industry agglomeration, sub-national institutions and the profitability of foreign subsidiaries

This study investigates the impact of agglomeration and its interaction with subnational institutions on the profitability of multinational enterprises (MNEs) subsidiaries operating in an emerging economy. We argue that in an emerging economy like China, competition in product and factor markets is more intense between foreign firms than between foreign and domestic firms owing to market segmentation. Consequently, agglomerating with other foreign firms has negative impact on the profitability of foreign subsidiaries. In contrast, foreign firms agglomerating with domestic firms may reap gains owing to less competition and improved access to local resources and knowledge. We find that these effects are more pronounced to domestic-market-oriented foreign firms. Furthermore, sub-national institutions moderate the above relationships. Our arguments are supported by the empirical analysis based on a comprehensive dataset of foreign firms operating in China over the period of 1999-2005

Spectral Classification Based on Deep Learning Algorithms

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we proposed a new CNN architecture specially designed for 2D spectral data. Firstly, we collected the reflectance spectra of five samples using a portable optical fiber spectrometer and converted them into 2D matrix data to adapt to the deep learning algorithms’ feature extraction. Secondly, the number of convolutional layers and pooling layers were adjusted according to the characteristics of the spectral data to enhance the feature extraction ability. Finally, the discard rate selection principle of the dropout layer was determined by visual analysis to improve the classification accuracy. Experimental results demonstrate our CNN system, which has advantages over the traditional AlexNet, Unet, and support vector machine (SVM)-based approaches in many aspects, such as easy implementation, short time, higher accuracy, and strong robustness

Effect of <i>PACAP/PAC1R</i> on Follicle Development of Djungarian Hamster (<i>Phodopus sungorus</i>) with the Variation of Ambient Temperatures

In Phodopus sungorus, the relationship between pituitary adenylate cyclase-activating polypeptide (PACAP) and its receptor (PAC1R), follicle-stimulating hormone (FSH), and follicle development remains unclear. In this study, we found that the development of growing follicles and antral follicles were inhibited at low (8 °C, 14 °C) and high (29 °C) temperatures. Meanwhile, PACAP/PAC1R expression and follicle-stimulating hormone (FSH) serum concentration significantly decreased during ambient temperatures of 8 °C, 14 °C and 29 °C compared to 21 °C. Thus, ambient temperature may influence the expression of PACAP/PAC1R and the synthesis of FSH for involvement in follicle development. Moreover, PACAP/PAC1R had major functional elements including PKA/PKG and PKC phosphorylation sites, which may involve in the pathway of FSH synthesis through cAMP-PKA and its downstream signal pathway. Moreover, there was a significant positive correlation between the expression levels of PACAP/PAC1R and the number of the growing and antral follicles, as well as the serum FSH concentration and the number of antral follicles. However, there was no significant correlation between the expression levels of PACAP/PAC1R and the serum FSH concentration, indicating a complicated pathway between PACAP/PAC1R and FSH. In conclusion, ambient temperature affects the expression of PACAP/PAC1R and the serum FSH concentration. The expression of PACAP/PAC1R and the serum FSH concentration are correlated with follicle development, which implies that they are involved in follicle development, which will ultimately influence the reproduction of Phodopus sungorus. This study can lay the foundation for future investigation on the regulation mechanism of reproduction in Phodopus sungorus

Geographic Distance Affects Dispersal of the Patchy Distributed Greater Long-Tailed Hamster (<i>Tscherskia triton</i>)

<div><p>Dispersal is a fundamental process in ecology influencing the genetic structure and the viability of populations. Understanding how variable factors influence the dispersal of the population is becoming an important question in animal ecology. To date, geographic distance and geographic barriers are often considered as main factors impacting dispersal, but their effects are variable depending on different conditions. In general, geographic barriers affect more significantly than geographic distance on dispersal. In rapidly expanding populations, however, geographic barriers have less effect on dispersal than geographic distance. The effects of both geographic distance and geographic barriers in low-density populations with patchy distributions are poorly understood. By using a panel of 10 microsatellite loci we investigated the genetic structure of three patchy-distributed populations of the Greater long-tailed hamster (<i>Tscherskia triton</i>) from Raoyang, Guan and Shunyi counties of the North China Plain. The results showed that (i) high genetic diversity and differentiation exist in three geographic populations with patchy distributions; (ii) gene flow occurs among these three populations with physical barriers of Beijing city and Hutuo River, which potentially restricted the dispersal of the animal; (iii) the gene flow is negatively correlated with the geographic distance, while the genetic distance shows the positive correlation. Our results suggest that the effect of the physical barriers is conditional-dependent, including barrier capacity or individual potentially dispersal ability. Geographic distance also acts as an important factor affecting dispersal for the patchy distributed geographic populations. So, gene flow is effective, even at relatively long distances, in balancing the effect of geographic barrier in this study.</p></div