Sustainable livelihood capital and climate change adaptation in Pakistan's agriculture: Structural equation modeling analysis in the VIABLE framework.

This study aims to assess the role of sustainable livelihood capital, the mediation of investments and farming purposes, and the moderation of climatic and non-climatic factors in the adaptation process, particularly in the aspects of Crop, Farm, Irrigation, and Economic Management. Moreover, guided by the VIABLE (Values and Investments for Agent-Based Interaction and Learning in Environmental Systems) framework, we analyze stakeholders' actions, priorities, and goals in the climate change adaptation process. A structured questionnaire was designed based on a five-point Likert scale covering the concepts of livelihood capital, climate change adaptation, investment priorities, farming constraints, and farmers' decision-making factors. Field data were collected from 800 farmers during December 2021 to February 2022 in the irrigated agricultural regions in the Indus Plain of the Punjab and Sindh provinces, Pakistan. We employed the Partial Least Square Structural Equation Modeling approach to the VIABLE framework (VIABLE-SEM) to analyze the collected data. The results confirm livelihood capital as the most significant determinant (beta = 0.57, effect size = 0.503) for farmers' adaptation strategies in the Indus plain. Other variables, such as the principal purpose of farming, available investment options, natural and human constraints, appear less important. We identified 13 significant viability pathways that show investment priorities, farming purposes, and constraints faced by the farmers in climate change adaptation. The study also found that non-climatic factors negatively influence (beta = -0.156) the relationship between capital and adaptation, while climatic factors positively influence (beta = 0.050) this relationship. Interestingly, the presence of these influencing factors increases the adaptive capacity of farmers. These findings have important implications for policymakers and researchers in designing and implementing effective climate change adaptation strategies in the agricultural sector of Pakistan.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Epidemiological characteristics and risk factors for cystic and alveolar echinococcosis in China: an analysis of a national population-based field survey.

BACKGROUND: Human cystic and alveolar echinococcosis are neglected tropical diseases that WHO has prioritized for control in recent years. Both diseases impose substantial burdens on public health and the socio-economy in China. In this study, which is based on the national echinococcosis survey from 2012 to 2016, we aim to describe the spatial prevalence and demographic characteristics of cystic and alveolar echinococcosis infections in humans and assess the impact of environmental, biological and social factors on both types of the disease. METHODS: We computed the sex-, age group-, occupation- and education level-specific prevalences of cystic and alveolar echinococcosis at national and sub-national levels. We mapped the geographical distribution of echinococcosis prevalence at the province, city and county levels. Finally, by analyzing the county-level echinococcosis cases combined with a range of associated environmental, biological and social factors, we identified and quantified the potential risk factors for echinococcosis using a generalized linear model. RESULTS: A total of 1,150,723 residents were selected and included in the national echinococcosis survey between 2012 and 2016, of whom 4161 and 1055 tested positive for cystic and alveolar echinococcosis, respectively. Female gender, older age, occupation at herdsman, occupation as religious worker and illiteracy were identified as risk factors for both types of echinococcosis. The prevalence of echinococcosis was found to vary geographically, with areas of high endemicity observed in the Tibetan Plateau region. Cystic echinococcosis prevalence was positively correlated with cattle density, cattle prevalence, dog density, dog prevalence, number of livestock slaughtered, elevation and grass area, and negatively associated with temperature and gross domestic product (GDP). Alveolar echinococcosis prevalence was positively correlated with precipitation, level of awareness, elevation, rodent density and rodent prevalence, and negatively correlated with forest area, temperature and GDP. Our results also implied that drinking water sources are significantly associated with both diseases. CONCLUSIONS: The results of this study provide a comprehensive understanding of geographical patterns, demographic characteristics and risk factors of cystic and alveolar echinococcosis in China. This important information will contribute towards developing targeted prevention measures and controlling diseases from the public health perspective.

Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil.

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia.

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

Modelling armed conflict risk under climate change with machine learning and time-series data.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Evaluating economic and ecological management to determine the economic size of pastoral units for different climatic zones in the northeast of Iran.

Stocking rate and rangeland area are key variables to provide the livelihood of herders in different climatic zones. To evaluate the economic and ecological management of pastoral units, this study aims to determine the optimal economic size of pastoral units for livestock grazing use considering the ecological capacity of semi-arid rangelands in different climatic scenarios. Therefore, 12 pastoral units (an area of 47,355 ha) were selected in two climatic zones (summer and winter rangelands) in the Kalat region of Razavi Khorasan Province, Iran. By measuring forage production, carrying capacity was calculated. Based on the results, the costs of traditional livestock management in winter rangelands are higher than those of summer rangelands. Moreover, the current size of the assigned rangeland, especially the summer rangeland, is lower than that of the economic justification. The results emphasize that rangeland-based livestock husbandry cannot create a good livelihood for herders in the region, and it is necessary to pay special attention to other services and aspects, despite the existing ecological and socio-economic complexities. In this regard, providing multi-purpose rangeland use and useable technologies to better manage these areas is necessary to increase per capita household income and reduce the stocking rate in the region's rangelands. Ultimately, both increasing the level of available rangelands and reducing costs by applying new technology are required, as is the economic consideration of pastoral units by using rangelands for multiple purposes.

A life-cycle assessment framework for quantifying the carbon footprint of rural households based on survey data.

This paper presents the development of a life-cycle assessment (LCA) framework for quantifying the carbon footprint of individual households based on detailed household survey data. According to household consumption and production patterns, the comprehensive life-cycle assessment framework is designed with clarified life-cycle boundaries. The framework covers eight types of specific living activities of rural households generated from a large-scale household survey in the Three Gorges Reservoir area in China. Both carbon emissions and carbon sequestrations of households' consumption and production activities can be assessed, and eventually the integrative carbon footprint is evaluated. The LCA framework enables comprehensive and relatively precise assessment of carbon footprint at a household level and thus adds value to studies in the fields of carbon emission reduction, carbon neutrality and climate policies.

Cities on the Coast and Patterns of Movement between Population Growth and Diffusion.

Sea level rise and high-impact coastal hazards due to on-going and projected climate change dramatically affect many coastal urban areas worldwide, including those with the highest urbanization growth rates. To develop tailored coastal climate services that can inform decision makers on climate adaptation in coastal cities, a better understanding and modeling of multifaceted urban dynamics is important. We develop a coastal urban model family, where the population growth and urbanization rates are modeled in the framework of diffusion over the half-bounded and bounded domains, and apply the maximum entropy principle to the latter case. Population density distributions are derived analytically whenever possible. Steady-state wave solutions balancing the width of inhabited coastal zones, with the skewed distributions maximizing population entropy, might be responsible for the coastward migrations outstripping the demographic development of the hinterland. With appropriate modifications of boundary conditions, the developed family of diffusion models can describe coastal urban dynamics affected by climate change.

Directions for Research on Climate and Conflict.

The potential links between climate and conflict are well studied, yet disagreement about the specific mechanisms and their significance for societies persists. Here, we build on assessment of the relationship between climate and organized armed conflict to define crosscutting priorities for future directions of research. They include (1) deepening insight into climate-conflict linkages and conditions under which they manifest, (2) ambitiously integrating research designs, (3) systematically exploring future risks and response options, responsive to ongoing decision-making, and (4) evaluating the effectiveness of interventions to manage climate-conflict links. The implications of this expanding scientific domain unfold in real time.

An inverted U-shaped curve relating farmland vulnerability to biological disasters: Implications for sustainable intensification in China.

Sustainable farmland intensification is necessary in order to harmonize relationships between food security, socioeconomic development, and ecological civilization. However, the degradation of farmland sustainability because of biological disasters represents a major challenge if we are to achieve this intensification. Our understanding of farmland vulnerability to biological disasters (FVBD) remains relatively rudimentary and subjective, limiting its effectiveness as a tool for farmland sustainability analysis. Limited research has also been carried out on FVBD changes taking into account human decisions on farmland use. The aim of this study is to achieve a novel understanding of FVBD change and its implications for sustainable intensification using evidence from Chinese farmland use. A theoretical framework based on an inverted U-shaped curve that depicts FVBD as well as an assessment framework for FVBD were established using induced substitution of agricultural production. Across China and considering 15 provincial districts with scarce farmland, the relationship between FVBD and socio-economic development was identified as consistent with an inverted U-shaped curve at both national and provincial levels. FVBD values in 2016 across Southern China, on the Huang-Huai-Hai Plain, and on the middle-lower Yangtze Plain were 45.44, 40.58, and 37.22, respectively. These values also decreased in provinces on the middle-lower Yangtze Plain between 1995 and 2016, but increased markedly across provinces in Southern China and on the Huang-Huai-Hai Plain. Contributions to FVBD changes during stages of growth and decline were also analyzed between 1995 and 2016. An inverted U-shaped curve was effective in investigating the responses of farmland sustainability to a range of alternative future socioeconomic development pathways. Thus, in the Chinese settings, a typical country with scarce farmland, policies on FVBD control are essential if we are to promote sustainable farmland intensification. The findings of this work are important and present us with a new way to understand FVBD from a human perspective.

Climate as a risk factor for armed conflict.

Research findings on the relationship between climate and conflict are diverse and contested. Here we assess the current understanding of the relationship between climate and conflict, based on the structured judgments of experts from diverse disciplines. These experts agree that climate has affected organized armed conflict within countries. However, other drivers, such as low socioeconomic development and low capabilities of the state, are judged to be substantially more influential, and the mechanisms of climate-conflict linkages remain a key uncertainty. Intensifying climate change is estimated to increase future risks of conflict.

Farmer Perceptions of Climate Change, Observed Trends and Adaptation of Agriculture in Pakistan.

Farmers' willingness and ability to adapt agricultural systems depend on their knowledge about changes in climate and perceived risks of extreme events. Using cross-sectional data of 450 farmers collected from three agro-ecological zones of Punjab, Pakistan, this study investigates farmer perceptions of climate change and their agreement with observed climatic trends. In addition, this study explores the correlation between different adaptation stages (perceptions, intentions, and adaptation) and their key drivers using a Multivariate Probit Model. This study also explores the adaptation measures adopted by farmers. The results of the study show that the perceptions of increasing mean temperature match well with locally recorded data. However, a discrepancy is found in some cases between farmer perceptions of rainfall changes and local climate records. Moreover, education, experience, land tenure, land holdings, extension, cooperation, access to weather forecasting, and marketing information are the factors influencing the three adaptation stages. A strong association is found among the three adaptation stages. Particularly, the study confirms the hypothesis that accurate perceptions lead to stronger adaptation intentions compared to underestimated or no perceptions. Further, farmers prefer basic adaptation measures including changing crop varieties, input use and planting dates over advanced measures, such as planting shade trees, soil conservation, and crop diversification. The study recommends providing farmers, especially small landholders and tenants, easy access to information, institutional services and training on the use of advanced measures to reduce negative impacts of climate change at the farm level.

Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals.

Current mitigation efforts and existing future commitments are inadequate to accomplish the Paris Agreement temperature goals. In light of this, research and debate are intensifying on the possibilities of additionally employing proposed climate geoengineering technologies, either through atmospheric carbon dioxide removal or farther-reaching interventions altering the Earth's radiative energy budget. Although research indicates that several techniques may eventually have the physical potential to contribute to limiting climate change, all are in early stages of development, involve substantial uncertainties and risks, and raise ethical and governance dilemmas. Based on present knowledge, climate geoengineering techniques cannot be relied on to significantly contribute to meeting the Paris Agreement temperature goals.

The potential of volunteered geographic information to investigate peri-urbanization in the conservation zone of Mexico City.

Due to the availability of Web 2.0 technologies, volunteered geographic information (VGI) is on the rise. This new type of data is available on many topics and on different scales. Thus, it has become interesting for research. This article deals with the collective potential of VGI and remote sensing to detect peri-urbanization in the conservation zone of Mexico City. On the one hand, remote sensing identifies horizontal urban expansion, and on the other hand, VGI of ecological complaints provides data about informal settlements. This enables the combination of top-down approaches (remote sensing) and bottom-up approaches (ecological complaints). Within the analysis, we identify areas of high urbanization as well as complaint densities and bring them together in a multi-scale analysis using Geographic Information Systems (GIS). Furthermore, we investigate the influence of settlement patterns and main roads on the peri-urbanization process in Mexico City using OpenStreetMap. Peri-urbanization is detected especially in the transition zone between the urban and rural (conservation) area and near main roads as well as settlements.

Human and remote sensing data to investigate the frontiers of urbanization in the south of Mexico City.

The data presented here were originally collected for the article "Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing" (Rodriguez et al. 2017) [4]. They were divided into three databases (remote sensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remote sensing database was prepared as a result of a semi-automatic classification, dividing the land cover data into urban and non-urban classes. The second data set details an alternative view of the phenomena of urbanization by concentrating on illegal settlements in the conservation zone. It was based on voluntary complaints about environmental and land use offences filed at the Procuraduria Ambiental y del Ordenamiento Territorial del Distrito Federal (PAOT), which is a governmental entity responsible for reviewing and processing grievances on five basic topics: illegal land use, deterioration of green areas, waste, noise/vibrations, and animals. Anyone can file a PAOT complaint by phone, electronically, or in person. The complaint ends with a resolution, act of conciliation, or recommendation for action by other actors, such as the police or health office. The third data about unemployment was extracted from Mexico׳s National Census 2010 database available via public access.

Change in Environmental Benefits of Urban Land Use and Its Drivers in Chinese Cities, 2000-2010.

Driven by rising income and urban population growth, China has experienced rapid urban expansion since the 1980s. Urbanization can have positive effects on the urban environment; however, improvement of urban environment quality, especially its divergence between relatively developed and undeveloped cities in China, is currently a rather rudimentary and subjective issue. This study analyzed urban environmental benefits among China's prefectural cities based on their structure of urban land use in 2000 and 2010. First, we divided 347 prefectural cities into two groups, 81 coastal and capital cities in the relatively developed group (RD) and 266 other prefectural cities in the undeveloped group (RP). Then, we defined three areas of urban environmental benefits, including green infrastructure, industrial upgrade, and environmental management, and developed an assessment index system. Results showed that all prefectural cities saw improvement in urban environmental quality in 2000-2010. Although the RD cities had higher income and more population growth, they had less improvement than the RP cities during the same period. We also found that demographic and urban land agglomeration among RD cities restrained green infrastructure expansion, making green infrastructure unsuitable as a permanent solution to environmental improvement. It is therefore urgent for China to promote balanced improvement among the three areas of urban environmental benefits and between the RD and RP cities through regional differentiation policies.

Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan.

Pakistan is among the countries highly exposed and vulnerable to climate change. The country has experienced many severe floods, droughts and storms over the last decades. However, little research has focused on the investigation of vulnerability and adaptation to climate-related risks in Pakistan. Against this backdrop, this article investigates the farm level risk perceptions and different aspects of vulnerability to climate change including sensitivity and adaptive capacity at farm level in Pakistan. We interviewed a total of 450 farming households through structured questionnaires in three districts of Punjab province of Pakistan. This study identified a number of climate-related risks perceived by farm households such as extreme temperature events, insect attacks, animal diseases and crop pests. Limited water availability, high levels of poverty and a weak role of local government in providing proper infrastructure were the factors that make farmers more sensitive to climate-related risks. Uncertainty or reduction in crop and livestock yields; changed cropping calendars and water shortage were the major adverse impacts of climate-related risks reported by farmers in the study districts. Better crop production was reported as the only positive effect. Further, this study identified a number of farm level adaptation methods employed by farm households that include changes in crop variety, crop types, planting dates and input mix, depending upon the nature of the climate-related risks. Lack of resources, limited information, lack of finances and institutional support were some constraints that limit the adaptive capacity of farm households. This study also reveals a positive role of cooperation and negative role of conflict in the adaptation process. The study suggests to address the constraints to adaptation and to improve farm level cooperation through extended outreach and distribution of institutional services, particularly climate-specific farm advisory services.