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This paper examines the impact of extreme rainfall shocks on the performance in test scores of students living near at-risk urban areas in Brazil.

To identify the causal effect, we consider the exogenous variation of rainfall at the municipal level conditioned on the distance from the school to risk areas and the rainfall intensity in the school months.

The results suggest that extreme precipitation shocks, defined as a shock of at least three months of high-intensity rainfall, have an adverse impact on both math and language performance. Through a heterogeneous effects analysis, we find that the impact varies by student gender, with girls being more affected. In addition, among students who study near at-risk areas, those with better previous school performance and higher socioeconomic status are more negatively affected.

Our results suggest that extreme weather events can increase the differences in human capital accumulation between the population living near risk areas and those living more distant from these areas.

Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.

Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.

The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.

Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

Heavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.

The three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.

GSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.

A workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

The purpose of this paper is to empirically analyze the impact of chief executive officer (CEO) turnover on strategic change and explore the mediating role of organizational slack between them, as well as the moderating role and joint moderating role of top management team (TMT) external social network, ownership nature and industry type.

Based on the upper echelons theory, resource allocation theory and structuration theory, this paper takes the unbalanced panel data of A-share listed companies in Shanghai and Shenzhen Stock Exchanges of China from 2001 to 2018 as the research sample, uses ordinary least squares (OLS) regression method and fixed effect model to study the relationship between CEO turnover and strategic change, and focuses on the mediating mechanism and moderating mechanism between them.

The authors find that CEO turnover is positively related to strategic change. When a CEO turns over, a new CEO will initiate strategic change. Precipitation organizational slack plays a mediating role between CEO turnover and strategic change. Non-precipitation organizational slack has no mediating effect between CEO turnover and strategic change, which is embodied as “suppressing effects.” When the non-precipitation organizational slack variable is controlled, the impact of CEO turnover on strategic change will be enhanced. TMT external social network, ownership nature and industry type all negatively moderate the relationship between CEO turnover and strategic change. TMT external social network and ownership nature have a joint moderating effect between CEO turnover and strategic change. When TMT external social network is small, CEO turnover has a positive effect on strategic change in both state-owned enterprises and non-state-owned enterprises, but the promotion effect is stronger in non-state-owned enterprises. When TMT external social network is large, the positive effect of CEO turnover on strategic change in state-owned enterprises is from strong to weak, but in the non-state-owned enterprises is from weak to strong. TMT external social network and industry type have a joint moderating effect between CEO turnover and strategic change. When TMT external social network is small, CEO turnover has a positive impact on strategic change in high-tech enterprises and non-high-tech enterprises, but the promotion effect is stronger in non-high-tech enterprises. When TMT external social network is large, the positive impact of CEO turnover on strategic change in high-tech enterprises is from strong to weak, but in the non-high-tech enterprises is from weak to strong.

On the basis of previous studies, this paper further expands the research scope of the mechanism of CEO turnover on strategic change, echoing the research arguments of relevant scholars. At the same time, the research results reveal the mechanism of organizational slack, TMT external social network, ownership nature and industry type in the relationship between CEO turnover and strategic change, and further deepen the application of upper echelons theory, resources allocation theory and structuration theory in China. In addition, the research conclusions of this paper also provide reference value for Chinese enterprises in carrying out strategic change, promoting enterprise transformation and improving the level of corporate governance, and help to enhance the understanding and attention of Chinese enterprises to CEO turnover, organizational slack, TMT external social network, strategic change and corporate governance under the background of high-quality economic development.

In this study, the authors examine the push and pull effects of extreme weather events on migration among governorates in Egypt.

To estimate the effect of extreme weather events on internal migration, the authors use migration gravity models and data from the 1996 and 2006 Population and Housing Censuses. The authors measure weather extremes by the number of months in the past 36 months with temperatures or precipitation of a governorate below the 5th percentile and above the 95th percentile of the distribution of monthly temperatures or precipitation of the corresponding governorate during the period 1900–2006.

This study’s results suggest that high temperatures in the origin area act as a push factor. High-temperature extremes have a positive effect on out-migration. A 1% increase in the number of months with high-temperature extremes in the original governorate results in a 0.1% increase in the number of out-migrants.

The study suggests that people may respond to weather extremes through migration. However, climate migrants in Egypt may encounter several significant risks that authorities must address.

This study is one of the first attempts to measure the push and pull effect of weather extremes on migration in Egypt.

This study aims to investigate the influence of polyepoxysuccinic acid sodium (PESA), a green antiscalant, on the nucleation, crystallization and precipitation of magnesium phosphate.

The conductivity method was used to investigate the maximum relative supersaturation of magnesium phosphate across various PESA dosages. Subsequently, a magnesium phosphate scale was prepared using the static scale inhibition method (GB/T16632-1996) and then analyzed via scanning electron microscopy.

The findings showed that PESA extends the induction period of magnesium phosphate crystallization, reduces crystal growth rate and elevates the solution’s relative supersaturation. Notably, PESA exerts a low dosage effect on inhibition of the magnesium phosphate scale, with the optimal dosage identified at 10 mL. Scanning electron microscopy revealed that PESA dispenses a dispersing effect on the magnesium phosphate scale, generating numerous concave, convex and deeper pores on the scale particles’ surface, and thereby significantly enhancing the surface area, especially when using an antiscalant with variable dosages.

This study sheds new light on the impact of PESA, a green antiscalant, on the crystallization and precipitation of magnesium phosphate, thus paving the way for the development of enhanced and eco-friendly scale inhibition strategies in future applications.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This study aims to explore the smallholder farmers’ perceptions of climate change and its adaptation options (changing crop variety; improved crop and livestock; soil and water conservation [SWC]; and irrigation practices) and drought indices in the Dire Dawa Administration Zone, Eastern Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households for key informants and focus group discussions were used. This study used both descriptive statistics and an econometric model. The model was used to compute the determinants of climate adaptation options in the study area. Drought characterization was carried out by DrinC software.

The results revealed households adapted to selected adaptation options. The model results confirmed that education level, farm size, tropical livestock units (TLUs) and access to agricultural extension services have positive and significant impacts on changing crop variety by 0.0014%, 0.045%, 0.032% and 0.035%, respectively. The likelihood of farmers’ decisions to use adaptation strategies (family size, TLU, agricultural extension service and distance from the market) has positive and significant impacts on SWC. The reconnaissance drought index (RDI6) of ONDJFM and AMJJAS showed extreme and severe drought index values of −2.88 and −1.96, respectively.

This study used a locally adopted climate change adaptation intervention for smallholder farmers, revealing the importance of drought characterization indices both seasonally and annually.