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Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation. Machine learning methods provide an alternative that might improve the predictive effects. However, time series and extrapolation issues constrain methods such as random forests. We apply a simple thought experiment with national marginal GDP growth by aggregating subnational climate impact to alleviate the shortcomings in random forests. This paper uses random forests, multivariate cubic regression, and linear spline regression to examine the direct impacts of temperature on economic development and conducts a performance comparison of the methods. The model results indicate an optimal temperature of 15°C, 15°C or 21°C for each model. Furthermore, a thought experiment indicates that the marginal predictions of national GDP changes by approximately 1%, −3%, or −6% for models with 1°C warming. The performance comparison suggests that random forests have stable model performance and better prediction performance in bootstrapping. However, the extrapolation problem in random forests causes underestimation of climate impact in 5% of cells under 6°C warming. Overall, our results suggest that temperature should be considered in economic projections under climate change scenarios. We also suggest the use of more machine learning methods in climate impact assessment. [ABSTRACT FROM AUTHOR]

Atmospheric water vapour transport is one of the most active components of the hydrological cycle, which shows great effects on the regional climate variability, while it is largely affected by the large‐scale circulation systems. El Niño–Southern Oscillation (ENSO) shows a remarkable influence on the atmospheric water circulation via disrupting the normal/regular water vapour transport processes, producing the anomaly water vapour fluxes input from ocean to land (Qnet), and leading to regional precipitation anomalies. However, their quantitative impact has not been fully studied. This study quantified the effect of ENSO on the water vapour transport from ocean to land and identified its impact on tropical precipitation anomalies. The results showed that the global detrended Qnet displayed a significantly negative correlation with the strength of ENSO. The notable impact of ENSO on Qnet was largely affected by the different patterns of the atmospheric moisture circulation during El Niño and La Niña and was responsible for the change of the spatial distribution of precipitation over the Tropics. El Niño reduced precipitation over the Malay Archipelago and increased precipitation over south China, especially in boreal winter, while La Niña enhanced precipitation over the Malay Archipelago. This study identified the impacts of ENSO on the circulation and water vapour transport process and improved our understanding of the influencing mechanism of ENSO on regional precipitation anomalies, which has important significance on regional water resource management and disaster risk assessment, especially as the extreme climate events have become increasingly frequent. [ABSTRACT FROM AUTHOR]

Identifying and analyzing the urban–rural differences of social vulnerability to natural hazards is imperative to ensure that urbanization develops in a way that lessens the impacts of disasters and generate building resilient livelihoods in China. Using data from the 2000 and 2010 population censuses, this study conducted an assessment of the social vulnerability index (SVI) by applying the projection pursuit cluster model. The temporal and spatial changes of social vulnerability in urban and rural areas were then examined during China's rapid urbanization period. An index of urban–rural differences in social vulnerability (SVID) was derived, and the global and local Moran's I of the SVID were calculated to assess the spatial variation and association between the urban and rural SVI. In order to fully determine the impacts of urbanization in relation to social vulnerability, a spatial autoregressive model and Bivariate Moran's I between urbanization and SVI were both calculated. The urban and rural SVI both displayed a steadily decreasing trend from 2000 to 2010, although the urban SVI was always larger than the rural SVI in the same year. In 17.5% of the prefectures, the rural SVI was larger than the urban SVI in 2000, but was smaller than the urban SVI in 2010. About 12.6% of the urban areas in the prefectures became less vulnerable than rural areas over the study period, while in more than 51.73% of the prefectures the urban–rural SVI gap decreased over the same period. The SVID values in all prefectures had a significantly positive spatial autocorrelation and spatial clusters were apparent. Over time, social vulnerability to natural hazards at the prefecture-level displayed a gathering–scattering pattern across China. Though a regional variation of social vulnerability developed during China's rapid urbanization, the overall trend was for a steady reduction in social vulnerability in both urban and rural areas. [ABSTRACT FROM AUTHOR]

Wind energy exhibits strong fluctuations and intermittencies. The accurate prediction of wind speed is of considerable significance for the operation and maintenance of wind farms and the safety of the power grid. However, previous studies have often ignored the impact of data noise on trend prediction, and lacked effective data pre-processing methods and adaptive interval prediction schemes, resulting in poor prediction results. To improve the accuracy of prediction, this paper proposes a wind-speed combination interval prediction system based on a fuzzy strategy and neural network. This study used a fuzzy strategy to pre-process the data and proposed a joint optimization algorithm that uses multiple multi-objective metaheuristic algorithms to optimize the neural network jointly. An improved prediction hybrid algorithm was used to reconstruct the predicted results. Finally, based on the fuzzy theory and neural network, interval prediction schemes that can adapt to different situations were proposed. Wind-speed data from the Penglai Wind Farm in China were used for verification. The mean absolute percentage error of the wind-speed point prediction was 3.75%, and the prediction interval coverage probability of the wind-speed interval prediction was 97.92%. The experimental results showed that the proposed model not only outperforms other comparable models but also improves the prediction interval coverage probability by 176.4% compared with the baseline model. This proves that the integrated hybrid model can improve the accuracy and effectiveness of wind-speed prediction. The accurate interval prediction of wind speed can result in the complementary scheduling of renewable energy generation and the sustainable development of the energy field, and support intelligent upgrades. • Wind speed data is reconstructed using a fuzzy information granulation method. • Integration of multiple meta-heuristic algorithms for optimizing neural networks. • Integration of neural networks after multiple optimized structures. • An optimized backpropagation neural network has great potential for prediction. [ABSTRACT FROM AUTHOR]

Soil moisture is closely related to the hydrosphere, atmosphere, and biosphere, which makes it one of the most significant climate variables. Using data from the National Environmental Forecasting Center (NCEP), this paper analyzes the temporal and spatial characteristics of soil moisture at a depth of 0–10 cm in China for the period of 1948 to 2014. In addition, the soil moisture's possible interaction with air temperature is explored. Mainly using statistical analysis, the results showed that annual soil moisture decreased significantly (p < 0.01) in most areas. The tendency of decreasing soil moisture was relatively higher in spring and autumn than that in summer and winter. As to the national annual average soil moisture, there was a sudden change in the 1970s. The soil moisture had a relatively high value with a larger deviation before the abrupt change, but after that, the soil moisture was at a relatively low level with a smaller deviation. It was also found that the soil moisture at 0–10 cm showed a negative correlation with the 2-m air temperature above ground in the northern part of China, where the speed of the temperature rise was higher. The results are expected to help improve the understanding of the link between regional soil moisture variation and climate change. [ABSTRACT FROM AUTHOR]

Soil moisture and temperature are both significant factors for crop growth and development. They are also the main influencing factors of regional climate change. In the context of climate change, the relationship between soil moisture and soil temperature is not only important for explaining the mechanism of moisture and temperature interaction, but also provides scientific suggestions for agricultural production. Since the accurate measurement of soil moisture and temperature is difficult, their relationship remains poorly understood. Here, based on real-time field observation in a potato field in Wuchuan County, Inner Mongolia, China, the change characteristics of soil moisture and temperature under different water level treatments were analyzed, and their relationships were disclosed. The results show that there was an inverse proportional relationship between soil moisture and temperature. With an increase of soil moisture, soil temperature decreased. The basic relation between soil moisture and temperature took the form of reciprocal functions (Q = 4.2 × 103 V × (0.2 + W) × ΔT). The decline of soil moisture in the farmland will increase the soil temperature and has a negative impact on land–atmosphere interactions. The results can provide important insight into regional climate change mechanisms. [ABSTRACT FROM AUTHOR]

As a clean energy source, photovoltaic (PV) power generation best meets the current demand for energy transformation. In particular, industrial distributed PV projects in China have developed rapidly, forming a mature market trading mechanism, and the Chinese government's subsidy policy has strongly supported their development. However, lucrative government subsidies often lead to PV enterprises not paying attention to technological innovation and blind production. Therefore, to improve the efficiency of government subsidies, enhance the overall performance of the PV supply chain, and achieve the healthy and long-term development of the PV industry. In this paper, we consider the actual demand preference characteristics of users, and construct game models of the PV supply chain under different power structures. We explore the optimal decisions of the PV supply chain enterprises and the formulation of optimal government subsidies under different power structures. The results show that (1) reasonable government subsidies have an incentive effect, which can increase manufacturers' profits, service providers' profits and social welfare level, and improve the demand for PV products. (2) Different power structures significantly affect the government's optimal subsidy, PV system manufacturer (PSM)'s and PV system service provider (PSSP)'s optimal decision-makings, while it does not affect social welfare. (3) Government subsidies are much larger in the Stackelberg case than in the Nash equilibrium case. Government subsidies are positively proportional to PV product market size, user quality preference factor (QPF), user product coverage preference factor (PCPF), industrial unit electricity price, photovoltaic conversion efficiency, and effective generation hours, and inversely proportional to PV product discount sensitivity, production cost, quality innovation cost factor (QICF), and effort performance cost factor (EPCF). [ABSTRACT FROM AUTHOR]

Optimizing irrigation and nitrogen (N) fertilization cooperative application to coordinate the balance between agricultural production and ecological environment is a vital challenge for sustainable agricultural production. Here, a three-year field experiment was conducted from 2018 to 2020 in the semi-arid region of north China to investigate the effects of different irrigation amounts (W 1 , 50% ETc, ETc is the crop evapotranspiration; W 2 , 75% ETc; W 3 , 100% ETc; W 4 , 125% ETc) and nitrogen fertilization rates (F 1 , 75 kg·ha−1, F 2 , 150 kg·ha−1, F 3 , 225 kg·ha−1, F 4 , 300 kg·ha−1) on growth, yield, water and fertilizer use efficiency, and soil N residue of spring wheat by drip fertigation, and explore the optimal combination of irrigation amount and N fertilization rate based on a multi-objective optimization method. The results showed that leaf area index (LAI), dry matter accumulation, yield, water use efficiency (WUE) and economic return of spring wheat increased first and then decreased with increasing irrigation at the same level of N fertilization. These parameters peaked at W 3 , with W 1 performed poorly and low irrigation amount was detrimental to the function of fertilizer. At the same level of irrigation, LAI, dry matter accumulation, yield and economic return all demonstrated significant diminishing return effects with increasing N fertilization, whereas nitrogen partial factor productivity (NPFP) reduced with increasing N fertilization. Furthermore, with the simultaneous increase of irrigation and N fertilization, the accumulation of soil nitrate nitrogen (NO 3 -N) decreased in the 0 − 40 cm soil layer and increased in the 60 − 100 cm soil layer. The partial least squares path model (PLS-PM) revealed that irrigation had a direct positive effect on spring wheat yield rather than N fertilization, and irrigation had a direct negative effect on soil N residue. Based on the multi-objective optimization model established by multiple regression analysis and the elitist non-dominated sorting genetic algorithm (NSGA-II), the irrigation amount of 337.3–354.9 mm combined with the N fertilization rate of 181.2–198.6 kg·ha−1 could obtain the optimal comprehensive benefits of wheat yield, economic return, water and fertilizer utilization efficiency and environmental impact. This study can provide an important scientific basis for the management and optimization of irrigation and fertilization for spring wheat in semi-arid region of China and other similar regions around the world. • Irrigation had a direct positive effect on spring wheat yield rather than N fertilizer. • Excessive irrigation and N fertilizer increased soil nitrogen residue in deeper soil. • Water and nitrogen coupling improved economic return and reduced soil N residue. • The NSGA-II was applied to the optimization of irrigation and N fertilization. [ABSTRACT FROM AUTHOR]

• Effects of main tillage practices on crop yield, water consumption and water use efficiency of wheat and maize in Northern China were evaluated. • Subsoiling (SS) increased crop yield but consumed more water, and is suitable for winter wheat and summer maize in North-central China. • Mulching (M) significantly increased crop yield and conserved more soil water. • Ridge and Furrow Planting with Mulching (F–M) increased both wheat and maize yield, and was of more beneficial for maize than wheat. • M and F–M are suitable for spring maize in North-central China as well as wheat and maize in Northwest and Northeast China. Water deficiency seriously restricts the agricultural production in Northern China. Soil tillage practices can conserve water and increase yield effectively, but the regional applicability of soil tillage practices has not been systematically studied so far. It is significant to study the regional characteristics of tillage practices on water-conserving and yield-increasing so that the optimal practices for improving the crop water production and maintaining the agricultural sustainable development can be determined. This study applied the meta-analysis method to analyze results extracted from 156 peer-reviewed published papers conducted at 62 agricultural experimental sites for evaluating the effects of commonly used tillage practices, i.e., No-tillage (NT), Subsoiling (SS), Mulching (M), Ridge and Furrow Planting without Mulching (F), and Ridge and Furrow Planting with Mulching (F–M) on crop yield, water consumption and water use efficiency (WUE) of wheat and maize in Northern China. The results showed that NT only increased yield of winter wheat in North-central China and spring maize in Northeast China. SS increased wheat and maize yield by 16.3 ± 3.2 % and 9.2 ± 3.0 %, and increased water consumption by 8.4 ± 3.4 % and 1.8 ± 1.8 %, respectively. M increased the yield of wheat and maize by 14.9 ± 2.9 % and 17.7 ± 6.2 %, respectively, while it did not increase the water consumption. F increased the yield of wheat by 5.0 ± 1.1 %. F–M increased wheat and maize yield by 18.9 ± 6.3 % and 36.6 ± 11.8 %, respectively. This study recommends that, SS is suitable for winter wheat and summer maize in North-central China. M and F–M are suitable for spring maize in North-central China as well as wheat and maize in Northwest and Northeast China [ABSTRACT FROM AUTHOR]

Background: Child sexual abuse is a major public health problem with adverse consequences for victims' physical, mental, and reproductive health. This cross-sectional study aimed to determine the prevalence of child sexual abuse and its associated factors among 15- to 17-year-old adolescents in mainland China., Methods: From September 8, 2019 to January 17, 2020, a total of 48,660 participants were recruited by 58 colleges and universities across the whole country to complete the self-administered, structured, online questionnaire. This analysis was restricted to 3,215 adolescents aged between 15 and 17 years in mainland China. Chi-square tests and multivariate Logistic regression analyses were performed to identify individual, relationship, and community factors associated with child sexual abuse., Results: The overall prevalence of child sexual abuse was 12.0%. More specifically, 13.0% of girls and 10.6% of boys reported that they were sexually abused prior to 18 years of age. At the individual level, being female, sexual minority identity, younger age, and higher levels of knowledge, skills and self-efficacy regarding condom use were significantly related to increased odds of reporting sexual abuse. At the relationship and community level, adolescents from disrupted families and those entering into a marriage, having casual sexual partners, and having first intercourse at a younger age were more likely to report sexual abuse. On the contrary, those who had never discussed sex-related topics with their family members at home and were offered school-based sexuality education later (vs. earlier) were less likely to report sexual abuse., Conclusion: Multilevel prevention programs and strategies, including targeting adolescents with high-risk characteristics, educating young children and their parents about child sexual abuse prevention and optimizing the involvement of parents, school, community, society and government in comprehensive sexuality education, should be taken to reduce child sexual abuse among 15- to 17-year-old adolescents., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fu, Xu, Pan, Zhang, Zhang, Zhao, Lin, Ye, Liu, Lan, Luo, Wang, Zhu, Liao, Hong, Chen, Li, Yang, Zhao, Liu, Ruan, Yang and Wang.)

In 2017, the American College of Cardiology/American Heart Association (ACC/AHA) updated the Guideline of Prevention, Detection, Evaluation and Management and Management of High Blood Pressure (HBP) in Adults. The purpose of the current study was to evaluate the potential impact of the 2017ACC/AHA HBP guideline on hypertension prevalence, awareness, and control rates. The data were collected from Physical Examination Center of the Second Hospital of Hebei Medical University from January 2012 to December 2017 (N = 66 977), including demographic information and risk factors of hypertension. The hypertension prevalence, awareness, and control rates of people were evaluated according to the new guideline. Additionally, the factors related to hypertension prevalence were also assessed. According to previous HBP guideline, hypertension prevalence, awareness, and control rate were 30.54%, 44.33%, and 13.04%, respectively. However, when the 2017 ACC/AHA HBP guideline was introduced, the population with hypertension increased from 20 453 to 34 460, the hypertension prevalence rate increased from 30.54% to 51.45%, the awareness rate decreased from 44.33% to 26.31%, and the control rate declined from 13.04% to 2.72%. The most newly diagnosed hypertension patients were from the low-risk population with young age and without the above histories. The 2017ACC/AHA HBP guideline indicated that high hypertension prevalence rate still existed with a substantial increase, while the awareness and control rates were relatively lowered., (©2019 Wiley Periodicals, Inc.)