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In Ethiopia, large-scale agricultural investment (LAI) is promoted to foster adaptation to climate change among smallholder farmers by improving farming conditions and productivity. However, little has been known about this condition so far. Therefore, the aim of this paper is to study the implications of LAI for the capacity for climate change adaptation among smallholder farmers, based on a study conducted in Merti district in the Central Rift Valley of Ethiopia. An asset-based approach was used to measure the adaptive capacity of smallholders, via data collected from a household survey, undertaken among smallholder households who had, or had not, benefited, from LAI, and supported by key informant interviews and focus group discussions. Descriptive and inferential statistics and qualitative thematic analysis methods were used to analyze the data. The study found that the adaptive capacity of smallholder households was improved under the influence of LAI. LAI motivated and prompted farmers to use technologies that fostered climate change adaption, including the use of irrigation, improved crop varieties, chemical fertilizer, and engagement in alternative sources of income, which improved their food security status. But this technology and knowledge transfer mainly occurred through observation (learning by doing), not by capacity-building. However, both group of smallholders (within and without contact with the LAI) received similar benefits through government rural extension services. Therefore, the paper concludes that scaling up these technologies and alternative income sources could enhance the adaptive capacity of smallholders, but that LAIs should contribute actual technical and financial support as part of benefit-sharing or corporate responsibility. [ABSTRACT FROM AUTHOR]

AI chatbots such as ChatGPT help people produce texts. According to media reporting, these texts are also used for educational purposes. Thus, AI influences people's knowledge and perception of current issues. This paper examines the narrative of ChatGPT's stories on climate change. Our explorative analysis reveals that ChatGPT's stories on climate change show a relatively uniform structure and similar content. Generally, the narrative is in line with scientific knowledge on climate change; the stories convey no significant misinformation. However, specific topics in current debates on global warming are conspicuously missing. According to the ChatGPT narrative, humans as a species are responsible for climate change and specific economic activities or actors associated with carbon emissions play no role. Analogously, the social structuration of vulnerability to climate impacts and issues of climate justice are hardly addressed. ChatGPT's narrative consists of de-politicized stories that are highly optimistic about technological progress. [ABSTRACT FROM AUTHOR]

This paper assesses the robustness of the "natalist bias" of pollution control in de la Croix and Gosseries (J Environ Econ Manage 63:271–287, 2012), according to which taxing emissions encourage agents to shift from production to procreation, further deteriorating the environment and gradually impoverishing the next generations. We relax the assumptions that human capital does not depend on environmental quality and that society does not allocate resources to pollution control. Using a similar Overlapping Generations (OLG) growth model, our findings indicate that taxation does not necessarily encourage agents to permanently shift away from production because living under better environmental conditions enhances productivity through human capital formation. As the government increases the emissions price, agents reduce consumption and education spending, hurting output in the short term. However, in the long run, the reduction in emissions that follows taxation more than compensates for the initial adverse effects, provided that the sensitivity of human capital accumulation to environmental degradation is strong enough. Furthermore, as we increase the coefficient capturing such pollution externality, a Neimark–Sacker bifurcation occurs, making the system compatible with persistent endogenous fluctuations. [ABSTRACT FROM AUTHOR]

Sustainability in cotton production is inevitable because producing more cotton means more employment, economic acceleration, and industrial expansion. India, China, the United States, Brazil, and Pakistan contribute 74% of worldwide cotton production. Pakistan is contributing only 5%, despite the high potential of cotton. The average yield of cotton in Pakistan is stagnant at 570.99 kg hm−2, whereas it entails the highest cost of production among all other crops. The yield obtained in Pakistan is less than the potential, profitability is drastically lessening, and farmers are abandoning cotton for alternative kharif crops. Some traditional quantitative studies have unveiled different factors that affect cotton production. However, an in-depth qualitative study has never been conducted in Pakistan to explore the root causes of growing cotton crop failure. Following Moustakas's traditional phenomenological guidelines, this phenomenological study was conducted in the district of Rahim Yar Khan in the core cotton zone of Punjab province. A total of 10 interviews were conducted with purposively selected cotton growers based on a criterion: (i) having more than 10 years of cotton growing experience, (ii) being a cotton grower, and (iii) having at least 10 years of formal schooling. Interviews were conducted face to face on an interview guide. One interview lasted 45–50 min, and responses were recorded and analyzed using a thematic analysis approach. A total of 6 themes emerged from the collected data, including (i) climate change, (ii) varietal problems, (iii) pesticide usage, (iv) sense of institutional services, (v) attitude of farmers and (vi) soil health and environment. These six merging themes contributed to cotton crop failure and yield decline. The deep exploration further summarized that researchers, extensionists, and farmers need to seriously consider variety, sowing time, and the environment to revive cotton crops. The detailed recommendations and policy guidelines are presented in this paper, highlighting the cotton sector's research, development and investment areas. [ABSTRACT FROM AUTHOR]

Timely and accurate agricultural drought monitoring and drought-driven mechanism analysis in karst basins in the context of global warming are highly important for drought disaster monitoring and sustainable ecological development in a basin. In this study, based on MODIS data, meteorological and topographic data and land use data from 2001 to 2020, we used the Sen slope, the Mann–Kendall test and a geographic detector to explore the driving mechanisms of agricultural drought caused by climate change and human activities in the karst basin of southern China from 2001 to 2020. The results showed that (1) the spatial distribution of the TVDI in the karst basin in southern China has obvious regional characteristics, showing a decreasing trend from west to east. (2) According to the interannual trend of drought, the degree of drought in the South China karst basin exhibited a weakening trend over the last 20 years, with the most severe drought occurring in 2003. Regarding the seasonal change in the TVDI, drought in spring, summer and autumn exhibited a decreasing trend, while that in winter exhibited an increasing trend, and the drought intensity decreased in the following order: spring (0.58) > autumn (0.53) > summer (0.5) > winter (0.48). (3) Single-factor detection the results showed that rainfall, temperature and elevation were the main factors driving aridification in the study area; multifactor coupling (mean) drove drought in descending order: rainfall (q = 0.424) > temperature (q = 0.340) > elevation (q = 0.219) > land use (q = 0.188) > population density (q = 0.061) > slope (q = 0.057). Therefore, revealing the mechanism of agricultural drought in karst basins through the study of this paper has important theoretical significance and provides technical guidance for drought relief in karst areas. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate change, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in a more comprehensive socio-economic context. We consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how, by following different SSPs scenarios, the adopted strategies may influence the firm's default probability. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

Nature-Based Solutions concepts and practices are being used worldwide as part of attempts to address societal challenges but have also been criticised for not dealing with deeper transformations needed to face urgent issues including biodiversity loss, climate change and inclusion. In this paper, we explore how an inclusive, integrated and long-sighted approach, emphasising a more radical integration of nature within cities, might support the transformations needed to endure major contemporary challenges. Addressing important emerging critiques of Nature-Based Solutions, we consider the potential of a more incisive form of Nature-Based Thinking (NBT) in cities, based on more holistic perspectives. The paper draws on a reflective and iterative research process that engaged both the research and practice communities through a symposium and a series of futures workshops that together explored the potential of NBT to develop future nature-cities relations in Europe and Latin America. The results of the reflective process suggest that notions of nature with people—not for people— new organisational structures, and the intention and capacity to apply long-term perspectives, are needed when planning for NBS interventions aimed at sustainable urban development. This includes developing a cultural-structural change based on new and inclusive understandings of human–nature relations, and novel governance paradigms that allow cross-sectoral coordination and engagement of local stakeholders beyond formal organisational structures. [ABSTRACT FROM AUTHOR]

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Nature-based Solutions (NbS) are integral to efforts to keep global warming below 2°C in accordance with the United Nations' 2015 Paris Agreement on Climate Change. Yet the transboundary governance dimensions of NbS remain unclear and largely undocumented. In Southeast Asia, NbS have emphasised the conservation and/ or sustainable commodification of carbon sinks found in terrestrial and mangrove forests, seagrass meadows, peatlands and agricultural soils. Mostly project-driven and fixed-term, these "solutions" have often failed to meet their social and ecological objectives. Increasingly, they have added to cross-border problems of: (1) displaced carbon emissions; and (2) economic migration and societal dispossession. This perspective paper delineates a transboundary governance research agenda to mitigate these trade-offs and enhance the co-benefits of NbS in carbon sinks. Building on NbS literature, it identifies cross-sector, multi-scalar and interdisciplinary pathways to improve transboundary cooperation, inclusion and equity in carbon sink governance in varying Southeast Asian contexts. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

In Finland, agriculture is practiced in variable growing conditions that are further challenged by climate change and under external pressure caused by international competition, changing consumer preferences and demands, and the renewal of the European Union Common Agriculture Policy and Paris Agreement climate targets. Futures studies in agriculture have focused on scenario building with expert panels, though usually without farmers' involvement. This study focuses on farmers' views of shaping the future of Finnish agriculture. It builds on the disaggregative Delphi method and combines interviews and a representative survey of Finnish farmers. The study is based on a bottom-up process in which a farmer panel of 20 farms defined their views of the future in a semi-structured thematic interview. The views were turned into statements in a structured survey sent to the second panel, which comprised all Finnish farmers who had received agricultural subsidies in 2016, with 4401 respondents. The results were analyzed using quantitative factor analysis, which produced five future images for Finnish agriculture. The images were shown to the original farmer panel for reflection. They considered the probability of each future image and their own role in it. Technology solves as a future image was most favored by farmers, followed by Ecological and specializing small-scale production image. Business-as-usual was the least likely future images according to the interviewed farmers. This paper describes the process and discusses both methodological benefits and pitfalls, as well as farmers' future views of the forthcoming decades. [ABSTRACT FROM AUTHOR]

Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

This paper presents a validation of outputs from some GCMs of the CMIP6 project when used to assess climate projection and hydrological flows at a catchment scale for the case study area of the Lombardy region (Northern Italy). The modeling chain consists of (i) a choice of climatic scenarios from 10 GCMs of the CMIP6, (ii) the application of a stochastic downscaling procedure to make projections usable at the local scale, and (iii) the use of a semi-distributed physically based hydrological model Poli-Hydro for the generation of hydrological scenarios. Data on observed precipitation and temperature were collected from automatic weather stations, and the hydrological budget of four target catchments within the study area was assessed using Poli-Hydro. An ex-post (back-casting) analysis was performed upon the control data series from the GCMs by comparing statistics of relevant climate variables and model-simulated discharges against observed counterparts during the historical period 2002–2014. Then, during 2015–2021, the goodness of projections was assessed using confidence intervals. Our results show that the accuracy of GCMs in representing regional climate is not always reflected in a credible evaluation of local hydrology. The validation of climate patterns provides somewhat poor results; thus, the interaction among climate and hydrology needs to be explored carefully to warrant the credibility of hydrological scenarios. Overall, the spatial and temporal consistency of GCM projections, as explored here climatically and hydrologically, provides a clue about their dependability for basin scale management. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

This article discusses the role of soil microorganisms in the dynamics of soil organic matter (SOM) and how it is affected by land use and climate change. The authors highlight recent research that has shifted the understanding of SOM dynamics, emphasizing the importance of microbially derived SOM, microbial necromass, and root biomass and exudates. The article also presents five papers that investigate microbial control of SOM dynamics under different land uses and management practices. The findings suggest that microbial carbon use efficiency (CUE) is a key factor in balancing SOC accumulation and loss. The article concludes by emphasizing the need for a deeper understanding of microbial control of SOM dynamics in order to develop effective strategies for climate change adaptation and mitigation. [Extracted from the article]

The intensity and frequency of climate extremes such as heat waves, droughts and extreme rainfall events are projected to rise. This will increase the severity of their impacts across socio-ecological systems. Economic sectors such as nature-based tourism become more vulnerable because of their reliance on climate and natural capital as key resources. While attempts have been made to understand how climate change may impact tourists and the industry itself, little is known about the same on tourism-dependent communities. This paper determines the extent to which tourism-dependent communities are vulnerable to climate change in the Okavango Delta, Botswana, to enhance their wider livelihood the development of strategies for improving adaptive capacity, resilience, and reduced exposure sensitivities. A household survey of 172 households was conducted in three purposively selected villages of Mababe, Sankuyo and Khwai, actively involved in community-based tourism for their socio-economic development. Information sourced related to livelihood options, peoples’ resilience, local risks, and hazards. The data was analysed using descriptive statistics and thematic analysis. The results indicate that respondents have observed climatic changes over the years such as increased temperatures, decreased rainfall, and increased frequencies of extreme events. The respondents attributed changes in natural capital to these observed climatic conditions in the form of desiccation, dwindling populations of some wildlife species, decreased fish stocks and reduced vegetation cover. This renders the tourism-dependent communities vulnerable as their livelihood is threatened. The paper thus concludes that climate change adaptation is an urgent priority for local communities who are already exposed to existing climatic and non-climatic stresses. [ABSTRACT FROM AUTHOR]

Anthropic structures have been deployed in aquatic environments for the most variable purposes. Here, we review the use of artificial aquatic habitats (AAHs) worldwide through a systematic and comprehensive search of peer-reviewed literature. We aimed to synthesize the knowledge on the subject, identify gaps, and propose new perspectives for future research. In total, 460 peer-reviewed papers from 1972 to mid-2016 were included in our systematic review. Field studies carried out in marine temperate environments were predominant, whereas fewer efforts have been made in marine tropical and freshwater ecosystems. Experimental habitats made of concrete were the most common artificial structures. Visual census was the most used sampling method, and there was a dominance of short-term (< 12 months) and shallow waters (< 5 m depth) studies. Fish was the most studied group (18%), whereas tunicates encompassed the large proportion of non-native species associated with AAHs (32.9%). Although idealized for the promotion of biodiversity, AAHs have been serving as stepping stones for non-native invasive species, facilitating biotic homogenization by increasing the connectivity of hard-bottom systems. The scarcity of studies in coastal tropical and freshwater ecosystems, as well as the efforts with reduced temporal scales and restricted depths, calls for future research to better understand the role of artificial habitats, especially in climate change scenarios. In this sense, we provide a conceptual framework exploring major issues regarding AAHs, including biodiversity status, ecosystem services, and climate change, in order to guide future research and assist conservation measures. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the HEAT (Healthy Environments for AthleTes) project, which aims to understand the impact of environmental conditions on athlete health and performance during major sporting events such as long-distance running, cycling, and triathlons. In collaboration with the SAFER (Strategies to reduce Adverse medical events For the ExerciseR) initiative, the HEAT project carried out a field campaign at the 2022 Comrades Marathon in the KwaZulu-Natal province of South Africa. The measurement campaign deployed seven weather stations, seven PM2.5 monitors and one spore trap along the 90 km route to capture spatially representative measurements of complex micro-climates, allergenic aerospora, and particulate matter exposure. The results indicate that runners were exposed to moderate risk heat stress conditions. Novel findings from this initial campaign shows elevated and potentially harmful PM2.5 levels at spectator areas, possibly coinciding with small fire events around the race day festivities. Our findings show values PM2.5 levels over the WHO 24-h guidelines at all stations, while 2000 µg/m3 at two stations. However, the lack of an acute exposure standard means direct health impacts cannot be quantified in the context of a sport event. The HEAT project highlights important aspects of race day monitoring; regional scale climatology has an impact on the race day conditions, the microclimatic conditions (pollution and meteorology) are not necessarily captured by proximity instruments and direct environmental measurements are required to accurately capture conditions along the route. [ABSTRACT FROM AUTHOR]

Transport facilities are increasingly threatened by the climate-change related natural disasters in recent years. In order to incentivize the transport facility operators to engage in climate change adaptation, governments may provide subsidies to the adaptation investment. Due to the substantial uncertainties in the outcomes of the adaptation projects, the government can set up an “experiment” project, which can be used as a demonstrator. In this paper, we develop real-option game models to investigate the transport facility operators’ adaptation investment under static stochastic demand and time-varying stochastic demand. Meanwhile, the government’s optimal decisions on whether to set up a demonstrator and the related subsidy policies are also examined. We find that under static stochastic demand, the government prefers to set up a demonstrator if the marginal benefit of the adaptation capacity is larger than its marginal cost, or the demonstration effect is high enough. Under time-varying stochastic demand, the government prefers to set up a demonstrator when the demand volatility is low. Moreover, increasing demand volatility requires less (or more) subsidy, when the demonstrator is (or is not) set up. Increasing demand volatility leads to postponement of the adaptation timing and more adaptation investment, irrespective of whether there is a demonstrator. [ABSTRACT FROM AUTHOR]

An Erratum to this paper has been published: https://doi.org/10.1134/S1028334X23070188 [ABSTRACT FROM AUTHOR]