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In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

The purpose of this study is to investigate the influence of climate on marine and urban tourism using climate indices in four of Australia’s busiest cities: Sydney, Melbourne, Brisbane and Perth. Climate is operationalized using the previously validated Holiday Climate Index (HCI)-beach for marine tourism HCI-urban for city tourism; international airport arrivals are the tourism behavior of interest.

HCI-beach and-urban indices were calculated using climate data: thermal comfort, cloud cover, windspeed and precipitation. Autoregressive integrated moving average (ARIMA) models were calculated for airport arrivals only and airport arrivals with exogenous factors (i.e. HCI-beach and-urban).

Indices proved significant for each city where HCI-urban scores were more favorable on the aggregate than HCI-beach scores. HCI-beach improved model accuracy in Melbourne (3.11%), Sydney (15.77%) and Perth (37.38%); HCI-urban improved accuracy at Brisbane by 37.73%.

The primary limitation is that airport arrival data was only available monthly. Using aggregated arrivals also precludes explicitly determining recreational intentions among travelers.

Results demonstrate climate indices can improve forecast accuracy for actual tourism behaviors, including destination arrivals.

For tourists, results demonstrate the meteorological season and city where climate conditions are more or less favorable.

To the best of the author’s knowledge, this is the first known study to investigate the influence of climate indices on improving predictability of international arrival forecasts.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

The main aims of this study were to develop analytical scales for yachting tourism push-pull motivations and constraints, and analyze how these factors may influence the revisit intention of yachting tourists in China.

The analysis was conducted using the PLS-SEM, including the evaluation of measurement models and the structural models. SPSS18.0 and SmartPLS 3.3.5 software were used for statistical analysis.

We conducted a survey of 451 respondents who participate in yachting activities in Dalian, China and identified six push motivational factors (novelty and stimulation, sightseeing and leisure, sports and learning, social relationships, self-esteem and prestige and self-realization), three pull motivational factors (featured activities and services, destination environment, destination facilities) and two constraints (internal and external). Partial least squares structural equation modelling showed that all hypothesized interactions between identified factors were statistically significant and meaningful.

The push-pull-constraint model offers a new interpretation to the traditional push-pull model in theory, and the results contribute to local yacht industry sectors.

This study aims to analyze the policies and strategies used by governmental organizations to address the impacts of climate change in informal neighborhoods, kampungs, such as Bukit Duri and Melayu in Jakarta, Indonesia, focusing on canal and river flooding mitigation and infrastructure development. The research examines the displacement of residents due to the demolition of informal settlements along riverbanks, the role of different governmental organizations and the implications of these policies on affected communities.

This study uses the theoretical framework of environmental injustice to evaluate the strategies used by the Indonesian Government to address climate change adaptation in Jakarta, with a specific focus on the problem of flooding and its impact on displacement. By analyzing the history and outcomes of flood mitigation policies, this paper assesses the government’s strategies related to infrastructure, evacuation and socialization. In doing so, the study examines the social impact of these policies on affected communities. Furthermore, social listening and media analysis of Twitter data and various news outlets are conducted to gain insights into the living conditions and experiences of displaced residents in two public housing projects.

The study revealed the challenges faced by the government in implementing policies for climate change adaptation and flood mitigation in Jakarta, including a lack of community engagement with residents of the Kampungs in the decision-making process for relocation. Despite government efforts and providing low-cost apartments (rusuwana), the analysis sheds light on the various forms of injustice that result from the government’s approach to climate change adaptation in Jakarta.

This study examines social justice issues in Jakarta’s informal neighborhoods and explores locally driven efforts vs government-mandated policies for managing natural hazards and adapting to climate change.

Disasters are occurring worldwide, and Sri Lanka has also become a victim of multiple disasters. Though there is a disaster management mechanism from the national level to the local level in Sri Lanka, several gaps are identified in pre- and postdisaster management activities. Among them, the responsibilities of local-level stakeholders in flood management activities are not performing at a satisfactory level. Therefore, the flood damage from the previous events was too great. Accordingly, the purpose of this study is to assess the roles, responsibilities and performance of stakeholders involved in local-level flood risk management activities.

The Deduru Oya basin in Sri Lanka was selected as the study area. The study hypothesized that “ineffective roles, responsibilities and performance of local level stakeholders have weakened the flood risk management in the Deduru Oya basin.” The stratified random sampling method selected the sample (n = 425) from the flood-vulnerable population. Primary data were collected through a questionnaire survey, key informant interviews and field observations. Secondary data were collected from reports, work plans, journal papers and published maps. Descriptive data analysis methods and thematic data analysis methods were used in the study.

About 69% of the respondents state that they are not satisfied with the involvement of government agencies in local-level flood risk management activities, while 10% of the respondents state that they are satisfied. The thematic analysis reveals several gaps, such as the lack of reservations in the Deduru Oya, improper land use practices, no inundation areas are identified and mapped out and the effects of illegal sand mining in the Deduru Oya. In addition to that, the lack of legal power vested in local governments in disaster management and the weaknesses of current legislation on flood risk management are the other major courses of local-level ineffectiveness.

The current research was limited to studying the involvement of local-level stakeholders in flood risk management, but the involvement of subnational and national-level stakeholders should be studied in future research.

This paper examines the involvement of local-level stakeholders in flood risk management. The results of the study confirm that the roles and responsibilities of local-level stakeholders are not well-defined and mandated, and therefore, the performance of stakeholders involved is minimal. Therefore, the results highlight the need for a well-established community-based flood risk management mechanism.

This study aims to identify key built environment stakeholders in Sri Lanka and to understand their roles and responsibilities in tackling climate change. It also seeks to identify key challenges experienced by stakeholders.

Secondary data was systematically reviewed to identify key-built environment stakeholders in Sri Lanka. Thereafter, primary data was collected from a purposively selected sample of 40 built environment experts using a semi-structured interview schedule. The collected data was analyzed using the thematic analysis method.

The study identified built environment stakeholders in five sectors, namely, the state sector, the private sector, non-governmental organizations, academia and the general public. It was revealed that the state sector is responsible for policy formation and implementation, while the private sector acts as a financial facilitator. Academia generates and disseminates knowledge on climate change and community-based organizations provide inputs. Key skills and knowledge gaps of stakeholders include a lack of comprehension of the climate change theory and its concepts, lack of awareness on policies and legislation, lack of communication skills, language skills, digital proficiency and lack of integrated working skills. Unified action and equitable representation of all stakeholders is necessary to tackle climate change.

The original contribution of the paper is its emphasis on the importance of a multidisciplinary approach to the built environment in relation to climate change adaptations alongside its identification of key knowledge gaps among stakeholders.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.