Impact Of Weather Research Articles

Assessing the impact of dust events on the Holiday Climate Index in the Taklimakan Desert region.

As a significant sector within the tourism industry, desert tourism has developed rapidly in recent years, contributing significantly to local economic development. On the other hand, desert tourism is constantly influenced by the desert climate, characterized by high temperatures, aridity, and dust events. This study examines and analyzes the impact of dust events on the Holiday Climate Index (HCI) using an improved methodology. It incorporates comprehensive meteorological data including temperature, relative humidity, cloud cover, precipitation, wind speed and dust events of Tazhong, located in the heart of the Taklimakan Desert. The results indicate that the maximum mean monthly HCI dips from an ideal level (91) to a very good level (73), the minimum dips from good level (66) to a marginal level (47), and the annual comfortable days (HCI ≥ 80) decrease from 180.5 to 95.3 after considering the impacts of dust events. The corrective HCI indicates that autumn, especially October, offers relatively comfortable climatic conditions for tourism, with the mean monthly comfortable days reach 20.1. These findings can better guide desert tourism activities and also demonstrate that the impact of dust weather on tourism activities cannot be ignored.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Using loyalty card data to understand the impact of weather on click & collect behaviours in UK retailing

ABSTRACT This study assesses the influence of weather on Click & Collect (C&C) usage in England, examining differences between the day of order and day of collection, and how the weather influences the time taken to collect an order. Using sales and loyalty card data from a national retailer, we employ a series of gradient boosting models to analyse the influence of weather on daily order/collection totals, totals stratified by demographic group, and the time taken to collect an order. Weather is found to be consistently more influential on the day of order than the day of collection, however store location and thetiming of purchase have a higher relative influence than weather overall. The findings also evidence significant differences in influence between male and female customers and those aged above and below 65. Temperature and humidity are typically the most influential weather conditions, demonstrating a wide variation in effect on order totals. Temperature was also found to be significantly more influential than other weather conditions on the time taken to collect an order, with the highest temperatures leading to the longest collection times. Understanding the influences of different weather conditions on online consumption behaviours is of significant value to retailers, policy makers and associated stakeholders. However, it is an area that has been significantly under-researched compared to physical retailing, often due to a lack of reliable data. This study provides key insights into how different types of consumers behave on these platforms.

Climate Adaptation through Trade: Evidence and Mechanism from Heatwaves on Firms' Imports

Firms can adapt causally to heat waves by altering their import behaviors. Utilizing matched data from the China Customs Database, the China Industrial Enterprise Database, and the China National Meteorological Science Data Center, this paper investigates variations in firm-level imports on days marked by extreme heat. The study robustly establishes that, in comparison to the temperature range of [12 °C, 15 °C), extreme heat leads to increased imports by industrial firms. Additionally, for each additional day within a month featuring an average daily temperature falling within the ranges [24 °C, 27 °C), [27 °C, 30 °C), or [30 °C, +∞), monthly imports for firms increase cumulatively by 0.35%, 0.57%, and 0.56%, respectively. Notably, the impact of hot weather on non-state-owned labor-intensive firms is particularly pronounced. Our mechanistic analysis suggests that firms resort to heightened imports as a strategy for adapting to the warming climate, mitigating the elevated domestic production costs identified in existing studies. These findings bear relevance to the formulation of future “bottom-up” climate adaptation policies.

A machine learning framework for predicting weather impact on retail sales

The weather affects the sales of many retail products worldwide. As the weather becomes more erratic due to climate change, retail organizations must respond by incorporating weather information into their sales forecasting models. This study proposes a modeling framework for identifying, quantifying, and evaluating the use of weather information in forecasting models. The models are developed using several time-shifted weather features and machine-learning techniques. Our method is applied to a dataset encompassing individual products and product categories obtained from a large Canadian retail organization. We find that using weather information improves the accuracy of sales forecasts significantly, explaining up to an additional 47% of the variance for the individual products and up to an additional 56% for the product categories, on top of the variance explained by a baseline model. By analyzing the parameters of the trained models, we can also determine the importance and influence of each weather feature, including time-shifted features. Our research findings contribute to both the literature on forecasting in the retail sector and the decision-making of retail organizations. By comparing a model developed with and without weather information, the organization can better determine the value of weather in its planning. Customer expectations of future weather significantly influence sales and should be considered for future studies. Our work provides a basis for researchers and retail organizations to forecast sales of individual products using weather information.

Weather-related fragility modelling of critical infrastructure: a power and railway case study

Climate change has led to more frequent and severe extreme weather events, which impact critical infrastructure networks such as railway and power systems. Although infrastructure networks are interdependent, the analysis to understand the impact of weather events on infrastructure systems is usually performed in sector-specific silos. A methodology to examine how the same weather events affect different infrastructure sectors is presented, in order to understand cross-sectoral impact of extreme weather for interconnected regional infrastructure. Fragility modelling was used to examine the impact of temperature and rainfall on power and rail system failures using the West Midlands (in the UK) as a case study. The results demonstrated that the impact of temperature was broadly consistent across both infrastructure networks, showing less impact until specific upper and lower thresholds are passed; these thresholds were found to be similar for the different infrastructure networks evaluated, but railway infrastructure was found to be impacted more by lower temperatures. A growing correlation between the number of faults on power and railway systems was also found for both rainfall and temperature, indicating the value in coordinating preparation and planning efforts. For infrastructure operators and owners, regional resilience forums and other decision makers, this study provides an approach to assess the regional impact of extreme weather across multiple infrastructure sectors. The results give useful insights to inform the allocation of resources in response to extreme weather events.

Characteristics of Ozone Concentration in Shanghai and Its Associated Atmospheric Circulation Background During Summer Half-years from 2006 to 2021

It is of great importance to scientifically evaluate the impact of weather and climate conditions on the occurrence of O3 pollution in order to improve the accuracy of O3 pollution forecasts， as well as to reasonably control and reduce the adverse effects of O3 pollution. The characteristics of O3 concentration and climate background were analyzed based on daily O3 concentration data， meteorological factors， and NCEP/NCER reanalysis data from 2006 to 2021 in Shanghai. In addition， the differences in atmospheric circulation situations during years with anomalous O3 concentrations were compared and diagnosed from the perspective of climatology. Additionally， the monthly O3 concentration prediction model （seasonal autoregressive integrated moving average with exogenous regressors， SARIMAX） was further established by adding the key meteorological factors. The results indicated that both the whole-year average and summer half-year average O3 concentrations in Shanghai were increasing with fluctuation， and the summer half-year average was much higher than the annual average， up to 36.2%. Furthermore， there was a significant negative correlation between O3 concentration and wind speed （correlation coefficient of -0.826） and a significant positive correlation with the frequency of static wind and the number of days in which the low cloud cover was less than 20% （correlation coefficients of 0.836 and 0.724， respectively）. The monthly mean O3 concentration had a clear periodicity， showing a pattern with a high concentration in the middle period （April to September） and a low concentration at the beginning and end of the periods. High O3 concentration years （2013-2021） were accompanied by more polluted days， lower average wind speed， more small wind （≤1.5 m·s-1） days， more days of low cloud cover of less than 20%， more days of high temperature， higher direct solar radiation， and more sunshine hours. When the location of the stronger West Pacific subtropical high was westward and southward in the summer half-year， Shanghai was influenced by an anomalous westerly wind， which was not conducive to the transportation of clean air from the sea to Shanghai and thus led to the high concentration of O3 pollution. When the long wave radiation emitted from the ground was low in the summer half-year， it was favorable for the increase in ground temperature and caused a high concentration of O3 pollution. Adding direct solar radiation， maximum temperature， and wind speed as exogenous variables to the monthly O3 forecast model could significantly improve the effectiveness of the monthly forecast， with the root mean square error decreasing by 47.7% （from 22 to 11.5） and the correlation coefficient increasing by 11.2% （from 0.819 to 0.911）， which could be applied to the practical prediction of monthly O3 concentration.

Can infrastructure, built environment, and geographic factor negate weather impact on Strava cyclists?

Cycling participation is context-sensitive and weather condition is reportedly a significant factor. How weather affects cyclists with different demographics, trip purposes, and in the context of cycling infrastructure, built environment and geographic factors is less well understood by existing literature. This paper applies autoregressive models to explain difference in Strava cycling volume from the same hour of the previous day as a function of change in weather conditions, and day of the week; the contextual effect of cycling infrastructure, built environment and geographic factors is accounted for using interaction terms. We use Strava crowdsourced cycling data in Sydney, Australia, as a case study; commute and leisure cyclists, male and female, young and older cyclists are modeled separately. We find weather conditions have a statistically significant effect on cycling participation; rain, rainfall in the last 2 hours and wind are general deterrents to cycling. Physically separated cycling lanes reduce the adverse effect of precipitation on leisure cyclists and male cyclists but have little effect in retaining commute cyclists and female cyclists. The adverse effect of precipitation and wind on commute cycling is amplified in areas with good access to jobs, possibly due to the availability of better alternative modes of transport. Inland locations generally attenuate effects of windy conditions, except for young adults. This paper sheds light on factors attenuating adverse weather effects on cycling participation and provides useful guidance for future cycling infrastructure.

A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Impact of weather and climate advisories on agricultural outcomes in Pakistan

The earth is warming, and the frequency of extreme weather events have been rapidly growing globally with unprecedented consequences to farming communities. In principle, weather and climate information services (WCIS) can help farmers better manage their activities and farm level outcomes by supporting their decision-making with relevant and usable information to address the potential impacts of expected changing climate conditions. But, in practice, can the availability and use of WCIS help improve agricultural outcomes given the weather and climate related uncertainties? To understand the use and impact of WCIS in the cotton-wheat cropping areas of Pakistan, we conducted a multistage stratified clustered random sample of 612 farm households in the provinces of Punjab and Sindh. Over 55% of the farm households in the sample indicated that they used WCIS provided by the Pakistan Meteorological Department and other sources for making their agricultural decisions. Our analysis, however, suggests that the impact of using WCIS on major farm outcomes (i.e. farm revenue, costs, profits, and input usage) is not statistically significant when compared with those farm households not using WCIS (null result). This result is robust to different estimation techniques (i.e. ordinary least squares, instrumental variable approach, and propensity score matching method). From the focus group discussions, we gathered that farmers perceived WCIS as less reliable, often unclear, and difficult to understand as this information is not translated and transmitted in local languages. Addressing these issues can help reduce the impact of climate and weather variability on farm outcomes in those provinces as well as in Pakistan more generally. Our study suggests that, under uncertainty, emphasis should be on WCIS that farmers can rely on for making farming related decisions.

Management interventions of pearl millet systems for attaining cereal self-sufficiency in Senegal

Critical management interventions to target the yield potential for each environment are key to food security, increasing the resilience of current agricultural systems in Senegal. Cereal production is highly dependent on pearl millet (Pennisetum glaucum L.) rainfed systems as one of the major field crops for smallholders. This study aims to (i) quantify the production of pearl millet at the department level for the last quinquennial (until 2020), (ii) assess the impact of weather (temperature and precipitation) on the millet-based supply of cereal demand, and (iii) investigate, through crop modeling, the impact of millet-based supply of cereal demand by comparing recommended management interventions with smallholder-based strategies at the department level. Millet-based cereal supply–demand was estimated considering the observed population and the supply via the simulated pearl millet production (obtained using the APSIM-Millet model) at the department level from 1990 to 2021. High temperature and low precipitation occurrence presented a frequency of 35% across departments, leading to a reduction in millet production by roughly 6% relative to the normal average for 32 years. Adoption of recommended management showed the potential to increase the millet supply, more than doubling the current cereal supply, closing the current supply–demand gap (89 kg inhabitant−1). Achieving future cereal self-sufficiency will also require an intensification of other cereal production.

Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index “SI” (i.e., the ratio of percent severe crashes to percent exposure of a specific weather state during the crash period) was introduced to evaluate the impact of different real-time weather states on fatal and severe injury crashes. Furthermore, the standard mixed logit (MXL), correlated mixed logit (CMXL), and correlated mixed logit with heterogeneity in means (CMXLHM) models were fitted and compared to identify the risk factors contributing to crash injury severity while accounting for unobserved heterogeneity. The results showed that the CMXLHM model was statistically superior to the CMXL and MXL models based on various goodness-of-fit measures (e.g., Akaike information criterion “AIC” and McFadden pseudo R-squared). Results from the SI analysis and CMXLHM model showed that real-time weather-related factors (e.g., air temperature ≥ 70 0F and relative humidity ≥ 90 %) were significantly associated with higher severe injury likelihood. Further, driving under the influence (DUI), young drivers, and vehicle travel speed were associated with greater injury severities. On the other hand, presence of horizontal curve, passenger cars, and hourly traffic volume were associated with lower injury severity likelihood. The study outcomes can help in incident management by suggesting specific real-time weather-related states to feed to dynamic message signs (DMS) to enhance travelers’ safety along the interstates.

"Inherently Limited by Our Imaginations": Health Anxieties, Politics, and the History of the Climate Crisis

ABSTRACT: As global warming became a cause of concern in the 1980s, researchers and climate activists initially paid little attention to the possible health effects of a warmer world. This changed quickly between 1985 and 1989, when scientists working on contracts with the US Department of Energy and the Environmental Protection Agency extrapolated from existing knowledge about the impact of weather on health to speculate about how global warming would impact health. However, they downplayed the impact of their contributions by highlighting the uncertainty in their models and the adaptability of human societies. Since that time, physicians and other health scientists have maintained a steady drumbeat of warnings about the health effects of global warming. They have published widely in the medical literature and participated actively in international scientific collaborations. Their research has significantly increased the breadth and depth of climate-health science and shown that measurable impacts of global warming have already begun. But as the many climate crises of 2023 show, action against global warming remains inadequate. Is it still reasonable to hope that health advocacy will incite communities and politicians to act? The history of climate and health advocacy reveals many obstacles that must be overcome.

Learning from data to improve asphalt road construction processes

Asphalt road construction projects pose a lot of challenges in different disciplines, from the choice of construction methods, type and quantity of machines and personnel, pre-planning of all processes, ordering material from asphalt mixing plants, real-time logistics of handling material transport, and the asphalt paving and compaction process itself. Depending on the complexity and size of the construction site, preparation and execution can be influenced by a lot of unknown variables, like weather impact, material properties of the delivered asphalt or delays in the deliveries themselves. These circumstances might lead to irregularities in the asphalt material temperatures and thus suboptimal conditions for compaction or even construction stoppage - in any case, they lead to a reduction in quality of the built asphalt road. In recent years, a lot of mobile devices, sensors and software have been emerging as viable solutions in the construction industry to digitize and support these processes. Those real-time applications allow deeper insight into logistics or material properties on site and acquire data for downstream analysis. These endeavours in process optimization now benefit construction companies to increase efficiency, and for work safety and environmental aspects, it is crucial to optimize energy and CO2 consumption. Improved logistics and building roads with low-temperature asphalt will have to become a standard in road construction. The present study gives an overview of the digitalized processes in asphalt road construction with market-available solutions focusing on logistics data of the material delivery chain from mixing plant to paver, thermal imaging data of the built asphalt to validate asphalt temperatures, as well as machine and location data from asphalt paver and compaction rollers.

Optimizing renewable polygeneration: A synergetic approach harnessing solar and wind energy systems

Transitioning from fossil fuels to renewable energy ensures a sustainable and green future by limiting greenhouse gas emissions and their nuisance effects. This study proposes a polygeneration system that harnesses renewable energy to produce multiple energy vectors simultaneously and equipped with storage to mitigate the impact of intermittent weather. To perform dynamic analysis, proposed system has been modeled, optimized, and simulated using the transient simulation software TRNSYS®. The designed model has been optimized in GenOpt, linked with TrnOpt, under the weather conditions of Gujrat, Pakistan. The power system is energized by solar photovoltaic, thermal and wind power to produce cooling, heating, electricity, hydrogen, and oxygen as energy vectors, and also provides electric, hydrogen, and thermal storage. Prime movers utilized for energy conversion consists of evacuated glass tube collector, photovoltaic panels, wind energy conversion system, fuel cell, electrolyzer, and absorption chiller, which facilitate air-conditioning, space and water heating, electric power supply for electric vehicle, building load & national grid, hydrogen for internal combustion engines, fuel cell electric vehicle and industrial applications, as well as oxygen for hospitals. The study has shown that thermal collector has 68% efficiency, a solar fraction 0.78, peak outlet temperature 143.77 °C, and generates a total 1399656.25 kJ thermal energy. The wind and photovoltaic systems have maximum efficiencies values 52.24 % and 10.90 % respectively, resulting in the production of 44.8 MWh electric energy. Embracing the synergy of solar and wind power in a polygeneration system holds the key to a sustainable and eco-friendly future.

Management practices benefit endangered Poweshiek skipperling (Oarisma poweshiek) in Manitoba tall grass prairie

Poweshiek skipperling ( Oarisma poweshiek Parker, 1870) populations have declined precipitously in the past few decades, and their global range is now restricted to two isolated regions, one of which is the managed Manitoba tall grass prairie in Canada. In this paper, we used a decade of survey data from 2010 to 2019 to understand how habitat features, management practices, and extreme weather impact Poweshiek skipperling abundance in Manitoba. The strongest predictor of abundance was the density of black-eyed Susans ( Rudbeckia hirta L.), a primary nectar plant for adults. Poweshiek skipperling abundance also had a negative relationship with both the number of years since a burn occurred and the number of years since grazing occurred. Cumulative precipitation during their active period (May–June) had a negative relationship with skipperling abundance, whereas warm early springs and cool temperatures during the active period had positive relationships. These results suggest that management actions that maintain tall grass prairie habitat in an early successional stage (burning and grazing) and maintain important nectar sources benefit this population. In contrast, extreme weather events had varying effects on Poweshiek skipperling abundance. Results from this study inform ongoing management practices in the Manitoba tall grass prairie to support this endangered population.

Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather

This revolutionary study focuses on incorporating Artificial Intelligence (AI) and Parallel Distributed Computing (PDC) in agriculture prediction, with a specific interest in weather patterns. Modern technologies are instrumental in providing solutions to enhance agricultural efficiency and sustainability. Results from this research show that PDCs and AI have the potential to make crop yield predictions more accurate, detect and prevent diseases, and allocate resources efficiently. This paper emphasizes accurate weather forecasts for sustainable farming practices while considering multiple factors involved in decision-making processes according to expert opinions. These hybrid computing approaches are highly effective in generating detailed spatiotemporal information about agricultural systems, improving agricultural predictions' accuracy. Therefore, the study has far-reaching implications for industry players seeking to improve productivity using data-based techniques for sustainability purposes.

Predicting the magnitude and timing of peak electricity demand: A competition case study

AbstractAs weather dependence of the electricity network grows, there is an increasing need to predict the time at which the network peak load will occur. Improving forecasts of peak hour can lead to more accurate scheduling of generation as well as the ability to use flexibility to improve system utilisation or defer capital investment. While there are extensive benchmark models for forecasting electricity demand, their efficacy at forecasting the time or shape of the peak remains to be seen. Global forecasting competitions provide a unique opportunity to compare multiple methodologies under common performance criteria and incentives. The methodology and results are detailed from the Big Data and Energy Analytics Laboratory Challenge 2022 used by the team ‘peaky‐finders’ and investigates the suitability of using hourly methods to forecast daily peak magnitude, time, and shape. The resulting approach provides a reproducible ensemble benchmark against which to evaluate more complex methods. Results indicate that simple regression techniques can perform well and outperform more complicated methods during seasons with low hourly variability, however ensemble methods show higher accuracy overall. The results also highlight the significant impact of extreme weather on forecast accuracy, demonstrating the importance of forecasting processes that are resilient to extreme weather.

Evaluating the impact of agricultural production efficiency on sustainable development goals in coffee‐producing countries in Africa

AbstractClimate change has directly impacted coffee‐producing regions in Africa. This study uses the two stage Dynamic Network Directional Distance Function (DN‐DDF) and Malmquist Productivity Index (MPI) methods to evaluate the efficiency and productivity changes of agricultural production and sustainable development in 19 African coffee‐producing countries from 2014 to 2018. According to the empirical results, Central African Republic, Ethiopia, Liberia, Uganda, and Zambia have the highest overall efficiency. This study has three main contributions: (1) using a two‐stage model to evaluate the impact of agricultural production efficiency on sustainable development efficiency, (2) using a dynamic model with forest area as a cross‐period variable to reflect the real‐world phenomenon of coffee relying on forest mixed planting, and (3) objectively evaluating the impact of extreme weather on agricultural production by using rainfall as an exogenous variable to address previous research shortcomings. The results of this study can promote the improvement of agricultural production efficiency in African coffee‐producing countries and provide important reference value for sustainable development goals.