Adaptation Planning Research Articles

Prefecture-level health risk assessment for hot extremes in China

Evaluating prefecture-level health risks for hot extremes is the core of localized adaptation measures to mitigate negative heat-related impacts. Here, by applying the heat hazard-human exposure-social vulnerability framework, we assessed the prefecture-level health risk for summertime hot extremes in China, with multisource data from 329 prefecture units. Regions suffering very high and high levels of health risk were observed in several main basins and plains, including the Turpan Basin, the North China Plain, the middle-lower Yangtze River Plain, the Pearl River Delta, the Chengdu Plain, and the Wei River Valley. Health risk structures were identified, and 72 prefecture units (22%) were found in the risk zone of Hh-He-Hv (high heat hazard, high human exposure, and high social vulnerability), of which 44% of sites were distributed in the North China Plain and must be prioritized for adaptation. The risk components of human exposure were the main drivers of health risk and its spatial heterogeneity. For the southeastern region, social vulnerability plays a more significant role than heat hazard. Four types of social vulnerability were summarized and will be useful for targeted adaptation plans. The assessment process will be insightful and can be applied in other countries or on a global scale.

Panarchy to explore land use: a historical case study from the Peruvian Amazon

AbstractThe overexploitation of natural resources is an important driver of the global environmental crisis. The scientific community engages in an ongoing debate about the most suitable frameworks for analyzing trends in land use. In this paper, we use the conceptual lens of the adaptive cycle and panarchy to explore complex social–ecological dynamics in the Peruvian Amazon. Through an extensive literature review and interviews with key informants, we provide a historical analysis of extractive activities over the last two centuries in the department of Madre de Dios, Peru. We identify periods of growth, conservation, and sometimes release and reorganization for each resource and for the overall social–ecological system. We identify a general trend toward more diversification in extractive activities over time and inter-scale linkages between adaptive cycles of different time and space scales. Based on these results, we explore future development, hypothesize a possible domination of the agricultural sector, and suggest that land-use policies and management interventions should draw from past lessons to design adaptive land planning. Finally, we present new evidence highlighting the relevance of panarchy for understanding historical changes in complex social–ecological systems and demonstrate its utility in exploring both past land use and future development.

Establishing flood thresholds for sea level rise impact communication.

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States' coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.

Looking backwards to move forwards: insights for climate change adaptation from historical Māori relocation due to natural hazards in Aotearoa New Zealand

Climate change relocation of Indigenous communities is increasing globally. For Māori (the Indigenous peoples of Aotearoa New Zealand), many pā (complexes of significant structures) are at risk to climate change impacts with relocation as one potential adaptation option. For Indigenous peoples, this step is not taken lightly, as connection to place is imperative. The aim of this research is to highlight some histories of Māori communities relocating in response to natural hazards, providing insights from the past to help plan for future adaptation. To do this, we undertook a national-scale textual analysis and identified 51 examples of pā relocating in response to natural hazards since pre-1840. We then focused on a case study with members of two iwi (tribes), who relocated following the 1886 eruption of Mount Tarawera to identify enablers and barriers to relocation, and to draw insights to assist with Māori-led responses to climate change adaptation. We found that key enablers of relocation included whānau (family)-level decision-making, cultural norms of awhi (support), whanaungatanga (relationships), whakapapa (genealogical connection), koha (donation), tuku whenua (gifts of land), and mātauranga Māori (Māori knowledge). In summary, Māori and other Indigenous peoples have rich histories of relocations, utilising their own Indigenous knowledge, local implementation, and adaptability to natural hazards that can be implemented in our modern context.

A Bayesian analysis of adaptation of mountain grassland production to global change

Abstract In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: While an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified. We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2085. We built a Bayesian Belief Network (BBN) from long‐term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios. Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the conservation LULC scenario (−28% on average between 2020 and 2085), followed by the tourism development scenario (−7%) and the business‐as‐usual scenario (+3%). Climate change under representative concentration pathways (RCP) 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Synthesis and applications: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannual variability threaten the current grass‐based protected designation of origin (PDO) production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. Along with decreased total annual production due to decreasing grassland area compounded by more extreme climate change, the seasonal shift in production and increased interannual variability threaten the current grass‐based PDO production system. Further Bayesian modelling co‐developed with local stakeholders and experts could greatly contribute to adaptation planning of the regional production system.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

The Maharashtra River Basin's Flood-Drought-Water Scarcity Nexus

<p>India's droughts and floods, which have affected 8 million hectares yearly since the 1950s, make future adaptation and mitigation plans necessary, as well as an awareness of potential changes. In order to address the growing issues of flood and drought events in urban settings, the study emphasizes the importance of comprehensive water management strategies that ensure both routine water needs and severe event readiness. This study article recommends including drought and flood considerations in building codes and urban planning regulations to boost urban water resilience. The paper provides workable solutions for developing water-sensitive urban design, rainwater collecting, greywater reuse programs, and decentralized water management systems through a collection of case studies and strategy models. It also emphasizes the importance of teaching the population about the dangers of droughts and floods, water conservation methods, and emergency response procedures. The recommendations place a lot of emphasis on the need for a variety of policies that encourage cooperation between stakeholders, including local and state governments, business owners, community leaders, and experts. The policy recommendations include requests for green infrastructure, building standards for flood-resistant homes, water conservation requirements, and climate-responsive legislation, among other subjects. By encouraging sustainable water use behaviors, these concepts aim to increase urban resilience and make cities better prepared to mitigate the consequences of drought and flooding. These recommendations can aid local and state governments in creating a culture of preparedness for catastrophes, protecting communities, and fostering urban water resilience. This case study looks at the intricate relationships between a specific Maharashtrian river's floods, droughts, and water scarcity.</p>

Research on Port Risk Assessment Based on Various Meteorological Disasters

Within the framework of economic globalisation, ports serve as critical junctures in international trade and play a vital role. However, as infrastructure is closely linked to the natural environment, ports are highly susceptible to the impacts of meteorological disasters. Therefore, a comprehensive assessment of the risks posed by meteorological hazards to ports, establishing corresponding early warning mechanisms, and adopting reasonable response and recovery strategies, is paramount in ensuring the safe operation of ports and maintaining the stability of international trade. This study has comprehensively analysed historical data and identified the pre-established loss stratification system, improving the theoretical construct of “expected loss”. Additionally, this research has innovatively integrated the idea of preventative factors aligned with risk indicators. A quantitative algorithm was used to factor in the preventative factors within the computational procedure, deriving the weights pertinent to each risk indicator. This research aimed to reduce the subjectivity inherent in the weighting assignment process through such an approach, thereby enhancing disaster risk assessment’s scientific rigour and reliability. Moreover, it underscores the critical role of adaptive urban planning in enhancing the resilience of crucial economic nodes like ports, thereby contributing to the broader objectives of sustainable urban development.

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

The heat-related health impacts of extremely high temperatures have become increasingly severe worldwide. Quantitative information on these impacts is necessary to plan and implement adaptation measures. The present study aimed to predict the number of heatstroke ambulance calls (HAC) and ambulance occupancy rate of patients with heatstroke (AORPH) under extremely high temperatures in Tokyo, Japan. Extremely high temperatures were defined as the annual highest value of daily maximum temperature that occurs once every 30 y (30 y temp) or 50 y (50 y temp). Combinations of three greenhouse gas (GHG) emission scenarios and five climate models were adopted to predict the impacts during the base years (1985–2014), mid-21st century (2021–2050), and late 21st century (2071–2100). Furthermore, two types of populations were adopted: fixed and changing. In the case of 50 y temp, the HAC during the peak hour in the base years ranged from 52 to 130 persons across the climate models. In the case of a fixed population, in the late 21st century it ranged from 123 to 2618 persons across the climate models and GHG scenarios. In the case of a changing population, the corresponding values in late 21st century ranged from 128 to 3078 persons. The AORPH during the peak hour ranged from 32% to 80% during the base years for 50 y temp, assuming that all ambulances were dispatched for heatstroke only. In the late 21st century, it ranged from 75% to 1605% in the fixed population case. With population change, the corresponding values in late 21st century ranged from 83% to 1781%. In many scenarios, not all patients with heatstroke could be transported (AORPH > 100%) even if all ambulances have been dispatched. Therefore, adaptation measures that mitigate such an ambulance transport system collapse must be planned and implemented.

Refining Travel Suggestions using Python

In the era of personalized travel experiences, the need for intelligent and adaptive planning tools is paramount. This is developed using Python, harnesses the power of machine learning to analyse traveller preferences and patterns, offering customized destination suggestions. The core of this study is to demonstrate how machine learning can transform traditional travel planning into a more efficient, personalized experience. By integrating user data and preferences, the system proposes itineraries that not only align with individual interests but also enhance the overall travel experience. This details the development process, the machine learning techniques employed, and the efficacy of Python in creating a dynamic and responsive travel planning website. The goal is to enhance the user experience in planning trips by providing optimizing destination suggestions, considering factors like historical preferences, budget constraints, and time availability.

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources. 
Approach. Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates.
Main Results. Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively. 
Significance. This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15 Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.&#xD.

Navigating adaptive futures: analysing the scope of political possibilities for climate adaptation

ABSTRACT The growing scale and intensity of climate change poses a substantial challenge to the status quo of society and politics. Adapting to the risks associated with extreme weather events and changing climatic conditions will require the re-imagination of many aspects of politics and society. Therefore, climate change can be framed as a problem of imagination; one in which our relationship to the future is central to understanding how possibilities in the present are perceived. This research analyses public submissions made on New Zealand’s first draft National Adaptation Plan to understand how future climate adaptation is framed and imagined by different groups. In analysing submissions we identify and describe four thematic ‘adaptive futures’ that each argue for varied amounts of socio-political change from the status quo: data driven resilience; growth and opportunity; nature-society change; and flaxroots transformation. Underpinning these adaptive futures are emerging advocacy coalitions that seek to shape what is seen as possible, imaginatively, politically and materially. Our analysis also highlights how risks and opportunities are perceived by whom, and insights into attempts to delineate the boundaries of adaptive imagination and political possibility. Glossary of Māori terms: hapū: kinship group; iwi: extended kinship or tribal group; kaitiakitanga: intergenerational sustainability; kaupapa Māori: Māori approach, a philosophical doctrine, incorporating the knowledge, skills, attitudes and values of Māori society; kawa: protocols; mana: authority, dignity, control, governance & power; mana whenua: territorial rights, power from the land, authority over land or territory, jurisdiction over land or territory; te ao Māori: the Māori worldview; tikanga: correct procedures, lore & practises Definitions sourced from Blackett et al. 2022 & Te Aka Māori Dictionary

Last mile delivery of climate adaptation in Uganda, the need to integrate and decentralize climate adaptation planning and implementation processes

ABSTRACT Millions of people in Uganda are in need of urgent climate adaptation. The current central government-led top-down projects are inefficient and ineffective. These projects financed by multi-lateral global climate funds have with limited impact at community level. The Local Climate Living Adaptation facility (LoCAL) through its Performance-Based Climate Resilience Grants to local governments is much more efficient and effective. True last mile delivery of climate adaptation can be accomplished through the bottom-up District Development Planning process by assessing climate issues and capturing adaptation needs in village proposal sheets and turn them into Village Climate Adaptation Plans. To be able to rapidly provide locally led adaptation, systematically and continuously to the millions of people in need, integration of the three delivery mechanisms is proposed, where the millions of climate finance raised by the central government are channelled to district level as Performance-Based Climate Resilient Grants based on the adaptation needs captured in Village Climate Adaptation Plans.

Delimitation and Prioritization of Climate-Affected Areas Using the Example of a Post-Industrial City

This study adapted and tested an approach to identifying areas that are particularly vulnerable to climate-related hazards using the example of the post-industrial city of Knurów in Poland. This study applied a multi-criteria method using the analytical hierarchy process based on GIS map data. The analysis was divided into statistical regions defined for the city. Fifteen attributes were defined for each statistical region. The applied methods provided verified spatial information related to specific climate change hazards. The results showed that the most vulnerable areas were the areas with intensive development in the city center and in the southwestern part of the city. Among the 15 attributes, the most significant were T1 (number of inhabitants in zones with a higher potential thermal risk index by statistical district) with a value of 0.163, G2 (percentage of the sum of tree-shaded areas in built-up areas within the territory of a given statistical district) with 0.143 and H3 (number of buildings in areas of drainless basins and 100-year water). This method effectively identified the most vulnerable areas. The use of such a method can help in the preparation of planning documents and urban adaptation plans by determining the thermally and hydrologically vulnerable areas with the least developed green infrastructure as an exposure-mitigating factor.

Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system.

Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37%vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

The capacity of sentinel species to detect changes in environmental conditions and ecosystem structure

Abstract A major obstacle to preventing and reversing biodiversity loss in the Anthropocene lies in the scarcity of tools and data for monitoring the health and trajectory of ecosystems. Sentinel species can provide insight into unobserved ecosystem change, but it is unclear how effective sentinels are due to the local, context‐dependent nature of past research. Here, we present the first global evaluation on the effectiveness of sentinel species as indicators of ecosystem change. We conducted a meta‐analysis on 372 case studies to identify the ecological and methodological factors that correlate with the most effective sentinel species. Sentinel performance did not vary consistently across taxa or system; instead, sentinels that were more directly linked to ecosystem change due to their trophic role as predators were more effective. In addition, sentinel responses that were measured on a shorter timescale were more effective at indicating ecosystem change. Policy Implications. These results contribute to the longstanding debate on “what makes a good sentinel” and demonstrate the importance of both ecological and methodological factors when selecting sentinels to detect ecosystem change. For example, sentinel species which are trophically linked and measured on short timescales may be more effective for managers seeking to monitor ecosystem change than other species. By identifying effective traits for the use of sentinel species, scientists and policymakers will be able to develop rapid and adaptable management plans in response to global change.

A framework for climate change adaptation of port infrastructures

Adaptation of port infrastructures to climate change and sea level rise effects is highlighted as a key field among transportation systems’ lines of action for adaptation, given their highly exposed location in coastal areas and position as critical nodes in logistic chains and local, regional and national economies. The present work proposes an adaptation assessment framework that, based on a high-resolution compound climate risk assessment, identifies the main threats that climate change may pose to port performance, defines a set of optimized adaptation measures and characterizes constraints for implementation, and finally evaluates the applicability and effectiveness of these measures under diverse climate scenarios and different time frames. The framework is applied in a study case located in the northern coast of Spain. It is shown that the proposed methodology enables port managers and planners to develop tailor-fitted adaptation plans, providing tools to make them coherent with actual and future uncertain climate conditions.

Ornithological flight safety as a task of decision making in conditions of uncertainty

The problem of aircraft collision with birds (bird strike) is becoming more relevant with the growing trends of air transportation. According to the International Civil Aviation Organization (ICAO), over the seven-year period, 97751 aircraft collisions with animals in 105 countries around the world were recorded. In approximately half of the cases (56093 incidents), damage to the aircraft of various types is reported. According to some estimates, the annual damage from aircraft bird strikes is about 610 million US dollars. The article analyzes the effect of the aircraft bird strike threat (ornithological danger) on flight safety. The statistics of the aviation incidents with birds for the period 2010–2022 according to the figures from ICAO and the Federal Air Transport Agency of Russia are considered. The problems of risk assessment and ornithological flight safety and existing research works in this scientific field are analyzed. The main problems and shortcomings of the existing approaches are emphasized and discussed. A new concept for ornithological flight safety by dividing threats into those in a field of partial uncertainty and those in a field of deep uncertainty is proposed by the author. Making decisions in a particular field requires the use of specific tools. To solve the problem of ornithological flight safety under the threats in a field of deep uncertainty, a Dynamic Adaptive Planning (DAP) tool, that allows to develop and maintain the work of the plans with constant monitoring of the quality of their implementation is proposed. An example of the functioning of a decision-making algorithm and the assignment of critical points (triggers) to monitor the effectiveness of an airline’s plan with the aim of achieving specified ornithological flight safety indicators is given. An analysis of the results of the work showed that solving the problems in a field of deep uncertainty is a complex scientific task, however, the use of the new tools for scenario modeling will improve the quality of the Airline operation in providing ornithological flight safety, which will ultimately have a positive effect on reducing the risk of aviation incidents.

The Role of Expectancy Theory in the Organizational Management Process in Innovative Entrepreneurial Enterprises

The expectancy theory, originally put forth by psychologist Viktor Frankl, is a psychological framework that offers insights into human motivation and conduct. This theory suggests that the amount of effort individuals exert is directly influenced by their anticipation of particular outcomes and the degree to which those outcomes are personally appealing. Essentially, individuals adjust their behavior based on their assessment of the effort required to achieve specific outcomes and the value they attach to those outcomes. Within the realm of innovative entrepreneurship, organizational management faces a range of challenges. Among these, the rapidly evolving business environment stands out, necessitating constant adaptability and proactive planning. Additionally, attracting and maintaining talent is crucial, as the success of any enterprise heavily relies on its ability to recruit and retain key individuals. Furthermore, the establishment of an organizational culture has a profound impact on employee satisfaction, loyalty, and their capacity for innovation. Resource management also plays a pivotal role, particularly in the efficient allocation of funds, time, and technological resources. Lastly, competitive market forces and the need for strategic adjustments are realities that innovative entrepreneurial ventures must navigate, demanding continuous strategic realignment to maintain competitiveness. Against this backdrop of complexity and dynamism, the application of expectancy theory offers valuable insights for organizational management. By understanding and applying this theory, innovative entrepreneurial ventures can more effectively stimulate employee motivation, refine incentive systems, enhance goal management, and foster employee growth and engagement. To a certain extent, these functions can assist enterprises in overcoming various challenges in organizational management processes, thereby bolstering overall organizational performance.

A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

A significant and unprecedented increase of temperature has been recorded worldwide during the last decades, leading to the occurrence of numerous extreme events. Coastal areas, with their high population density, interconnected economic activities, fragile ecosystems, are particularly vulnerable to climate change impacts. These impacts can be intensified by the interactions of multiple hazards which operate at different spatio-temporal scales and affect exposure and vulnerability patterns. An integrated approach is here proposed to assess the relationship between risk factors and to evaluate the multiplicity of impacts that may affect the coastline. A new path to tackle these multi-risk events is offered by ML algorithms to effectively handle vast amounts of heterogenous data, and model complex non-linear relationships between multiple factors and feedback mechanisms. To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive during the 2009–2019 timeframe, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The MLP algorithm showed satisfactory performances (weighted-F1-score of 0.94) to estimate the relative importance of input features. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process.