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Freshwater availability is reducing globally, due to increasing demand with population growth and climate change and is disproportionately impacting developing countries. This study aims to investigate the dynamics of water access and consumption across all regions of Thailand with various characteristics and water systems. Understanding the relationship between institutional, economic and climate variables in Thailand’s water management is important for water scarcity planning. Our paper fills a gap in the literature by examining the determinants of water consumption and exploring potential water management policies.

The authors empirically analyze the determinants of water consumption in Thailand, including institutional, economic and climate variables. The authors use data sets from both metropolitan and provincial waterworks authorities (PWA), as well as economic and meteorological macro-level data. The authors also adopt an auto-regressive distributed lag (ARDL) model and a Johansen cointegration test to estimate short- and long-run effects of the variables on water consumption.

The authors confirm a negative relationship between water pricing and consumption and verify a positive relationship between economic growth and water consumption across most regions of Thailand. Furthermore, the authors reveal a clear relationship between climate factors and water consumption and an inverse relationship between income and water consumption in metropolitan area. Findings indicate that authorities, especially PWA, should examine high water use in agriculture and develop regulations to ensure equitable water distribution to sustain economic growth. The authors recommend that water prices are increased within specific income thresholds to prevent impacting low-income families and to secure higher public revenue. In pursuit of environmental sustainability, the authors also recommend increasing public awareness of freshwater scarcity through education programs and investment in water-saving technologies. Differences among regions should be considered when developing water management strategies, which could be monitored through the respective water boards.

This study provides deep insight into the key factors that drive both water prices and water consumption in poor and rich areas. The unique nature of the research indicated that the paper will be of interest to policymakers and the academic community. The findings are relevant for water consumption management in Thailand and other developing countries with similar characteristics.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

The purpose of this paper is to prepare flood hazard map and show the extent of flood hazard under climate change scenarios in Woybo River catchment. The hydraulic model, Hydrologic Engineering Center - River Analysis System (HEC-RAS) was used to simulate the floods under future climate scenarios. The impact of climate changes on severity of flooding was evaluated for the mid-term (2041–2070) and long-term (2071–2100) with relative to a baseline period (1971–2000).

Future climate scenarios were constructed from the bias corrected outputs of five regional climate models and the inflow hydrographs for 10, 25, 50 and 100 years design floods were derived from the flow which generated from HEC-hydrological modeling system; that was an input for the HEC-RAS model to generate the flood hazard maps in the catchment.

The results of this research show that 25.68% of the study area can be classified as very high hazard class while 28.56% of the area is under high hazard. It was also found that 20.20% is under moderate hazard and about 25.56% is under low hazard class in future under high emission scenario. The projected area to be flooded in far future relative to the baseline period is 66.3 ha of land which accounts for 62.82% from the total area. This study suggested that agricultural/crop land located at the right side of the Woybo River near the flood plain would be affected more with the 25, 50 and 100 years design floods.

Multiple climate models were assessed properly and the ensemble mean was used to prepare flood hazard map using HEC-RAS modeling.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Using Nigeria, as a point of reference, this study aims to explore the applicability of climatic variables as analytically valid factors for conceptual cost estimation. This is in view of the vastness and topographical alignment of Nigeria's landmass, which makes it a country of extreme climatic variability from north to south. As construction costs in Nigeria, similarly, tend to show a north-south alignment, the study's objective is to establish cost-estimating relationships (CERs) between the variability of climatic elements and the variance in construction cost, to arouse interest in the concept.

Deploying correlation analysis and multiple regression analysis, significant associations/relationships between meteorological variables and building cost for selected locations, following a North-South transect of the major climatic zones, are sought, to explain climate-induced construction cost variance. Validation of the regression model was carried out using variance analysis and the Mean Absolute Percentage Error of a different dataset.

Climatic indices of atmospheric moisture exhibited strong direct and partial correlations with construction costs, while sunshine hours and temperature were inversely correlated. The study further establishes statistically significant CERs between climatic variables and building cost in Nigeria, which accounted for 47.9% of the variance in construction cost across the climatic zones.

The study outcome provides a statistically valid platform for the development of more elaborate analytical costing models, for prototype buildings to be cited in disparate climatic settings.

This study establishes the statistical validity of climatic variables in constituting CERs for predicting construction costs in disparate climatic settings.

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.

This paper aims to develop a multi-hazard risk assessment method based on probability theory and a set of economic, social and environmental indicators, which considers the increase in hazards when they occur concurrently or consecutively.

Disaster risk assessment generally considers the impact and vulnerability of a single hazard to the affected location/object without considering the combination of multiple hazards occurring concurrently or consecutively. However, disasters are often closely related, occurring in combination or at the same time. Probability theory was used to assess multi-hazard, and a matrix method was used to assess the interaction of hazard vulnerabilities.

The results of the case study for the Mid-Central Coastal Region show that the proportions of districts at a very high class of multi-hazard, multi-vulnerabilities and multi-hazard risk are 81%, 89% and 82%, respectively. Multi-hazard risk level tends to decrease from North to South and from East to West. A total of 100% of coastal districts are at high to very high multi-hazard risk classes. The research results could assist in the development of disaster risk reduction programs towards sustainable development and support the management to reduce risks caused by multi-hazard.

The multi-risk assessment method developed in this study is based on published literature, allowing to compare quantitatively multiple risk caused by multi-hazard occurring concurrently or consecutively, in which, a relative increase in hazard and vulnerability is considered. The method includes the assessment of three components of disaster risk including multi-hazard, exposure and multi-vulnerability. Probability and Copula theories were used to assess multi-hazard, and a matrix method was used to assess the interaction intensity of multi-vulnerabilities in the system.

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.

This study examines the evidence of the impact of climate change on the financial performance of basic materials companies in Vietnam.

The research sample includes eighty-two basic materials companies listed on the Vietnamese stock market from 2003 to 2022. This study used one-way and two-way fixed-effects feasible generalized least squares (FGLS) estimation methods.

Climate change, measured through variables including changes in temperature, average rainfall, greenhouse gas emissions and rising sea levels, has a negative impact on the financial performance of companies in this industry. The study also found that, with rising temperatures, the financial performance of steel manufacturing companies decreased less than that of coal mining and forestry companies, but increasing greenhouse gases and rising sea levels reduced the financial performance of steel companies. We did not find evidence of any difference in the impact of climate change on the financial performance of basic materials companies before and after the UN Climate Change Conference (COP 21). This is a new finding, which is consistent with empirical studies in Vietnam and different from previous studies in that it provides new evidence on the impact of climate change on the financial performance of basic materials companies in the Vietnamese market and cross-checks the impact of climate change by sector and over time.

To the best of our knowledge, this is one of the first articles on climate change and the financial performance of basic materials companies.

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.