Population, Poverty, and Climate Change

Climate Change and Health: Strengthening the Evidence Base for Policy

The simulated low-frequency variability patterns of the atmospheric circulation, ranging from interannual to interdecadal timescales, are studied in an area encompassing southern South America. The experiment is a transient simulation performed with the IPSL CCM2 coupled global model, in which the greenhouse forcing is continuously increasing. The main modes of low-frequency variability are found to remain stationary throughout the simulation, suggesting they depend more on the internal dynamics of the atmospheric flow than on its external forcing. Inspection of the circulation regimes that represent the more recurrent patterns at interannual and interdecadal timescales showed that climate change manifests itself as a change in regime population, suggesting that the negative phase of the Antarctic Oscillation-like pattern becomes more frequented in a climate change scenario. Changes of regime occurrence are superimposed to a positive trend whose spatial pattern is reminiscent of the structure of the Antarctic Oscillation-mode of variability. Moreover, it resembles the spatial patterns of those regimes that show a significant change in population. The change in regime frequencies of the circulation patterns of low-frequency variability are in opposite phase with respect to the trend, thus, the behaviour of these patterns of variability, superimposed to a changing mean state, modulates the climate change signal. The analysis of the high frequencies, in terms of recurrent patterns representing intraseasonal and synoptic-scale of variability, shows no significant changes in regime characteristics, concerning both spatial and temporal behaviour.

The main aim of this study is to assess the water availability in the Huong River Basin, Central Vietnam under the impacts of climate and population changes. Regarding the climate change impacts assessment, two options were adopted to produce fine scale climate projections over the river basin. First, coarse scale projections of rainfall and temperature by the HadCM3 General Circulation Model (GCM) were downscaled statistically using the Statistical Downscaling Model (SDSM). The second approach used bias-corrected dynamically downscaled output of HadGEM3-RA Regional Climate Model (RCM). The HEC-HMS hydrologic model was used for simulating the rainfall-runoff response in association with climate forcing. Finally, water availability was evaluated using the Water Evaluation and Planning (WEAP) model by taking into account plausible population changes within the river basin. The results showed that the future temperature will rise by 0.2 to 3.5 °C and annual rainfall will increase by 1 to 8%. Water shortages in 2080s were non-existent if population projections were ignored, which is to be expected given the projected increase in rainfall. When projected population increases were considered, however, there were unmet urban water demands in districts that were previously self-sufficient. However, the shortages remain very small relative to the gross domestic demand that they give no cause for alarm. The big message here is that the hydrology of the Huong River Basin in Vietnam will cope with the most severe projected climate to meet its agricultural and domestic water supply obligations well into the future. • Temperature and rainfall are both projected to rise in the Huong River Basin. • Simulated water shortage was small for the projected climate. • Population changes accentuated water scarcity within the Basin.

In recent years, global hunger has begun to rise, returning to levels from a decade ago. Climate change is a key driver behind these recent rises and is one of the leading causes of severe food crises. When coupled with population growth and land use change, future climate variability is predicted to have profound impacts on global food security. We examine future global impacts of climate variability, population, and land use change on food security to 2050, using the modeling framework FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation). The model uses national food balance sheets (FBS) to determine mean per capita calories, hence incorporating an assumption that minimum dietary energy requirements (MDER) remain constant. To account for climate variability, we use two Representative Concentration Pathway (RCP) scenarios from the Intergovernmental Panel on Climate Change (IPCC), alongside three Shared Socio‐economic Pathway (SSP) scenarios incorporating land use and population change within the model. Our results indicate that SSP scenarios have a larger impact on future food insecurity, in particular because of projected changes in population. Countries with a projected decrease in population growth had higher food security, while those with a projected rapid population growth tended to experience the worst impacts on food security. Although climate change scenarios had an effect on future crop yields, population growth appeared to be the dominant driver of change in undernourishment prevalence. Therefore, strategies to mitigate the consequences of projected population growth, including improved maternal health care, increasing equality of access to food at the national level, closing the yield gap, and changes in trade patterns, are essential to ensuring severe future food insecurity is avoided.

Counting carbon costs

A cross-taxon analysis of the impact of climate change on abundance trends in central Europe

Climate change is an important theme which is addressed in many subjects nowadays in Finnish curriculum.  Young people are also very worried about and interested in climate change.  Understanding causes and effects of climate change is important as well is thinking about different ways to make a difference, what could be done and who should be doing it. Sustainable development is one important way of making the change towards cooler planet.I planned and held an online course of human geography with climate change point of view as part of a project Climate Change to Upper Secondary Schools. In this project three online courses were planned: 1,5 Degrees Life Skills for Young People, Human Geography (“in Climate Change Marinade”) and Climate Knowledge. The idea of online courses was to increase important future skills like critical thinking, problem solving, influencing the society, which would give strength to young people on their way to a better future.  My course Human Geography was the only one evaluated with a grade and directed only to older upper secondary school students. The other courses were evaluated only as approved or failed and they were open also to younger secondary school students.In my Human Geography course we studied the basics of human geography so that in every topic, were it population changes or natural resources, also how it is linked to climate change, was considered. Also, assignments that students did were linking together climate change and the topic studied.  During the time of corona pandemic part of students got accustomed to studying independently online, and interest in studying this way has increased. This is why, at least in Finland, online courses in schools seem to become more and more common. They are also cheap way to provide studying possibilities in a country where we have lot of small villages great distances apart one another.In my poster I’ll describe what kind of course my Human Geography was and how it was accomplished.

Demographic response of a neotropical migrant songbird to forest management and climate change scenarios

1. Phenological shifts caused by climate change are increasingly documented in wild populations by the widespread collection of datasets on reproductive timing and success. These phenological events are often inferred by examining changes in population abundance and age structure throughout the breeding season. However, the quantitative relationship between the observed proportion of juveniles over time and the underlying distribution of breeding times (e.g., onset of reproduction) and average reproductive success is often not explicitly addressed. In addition, potential biases introduced by selection on reproductive phenology or by the sampling design can affect our inference of reproductive phenology and success. 2. In this study, taking the example of bird monitoring, we proposed an analytical model to relate the proportion of juveniles in counts (e.g., mist-net captures) to the distribution of fledging dates and mean reproductive success in the population. We then infer laying dates from fledging dates, accounting for putative selection through fertility and/or juvenile survival to fledging. We simulated varying levels of variance, selection strength, and sampling effort. 3. Our simulation results show that our approach is able to recover the true mean and variance of laying dates and the mean reproductive success under ideal conditions (large sampling effort, no selection). It notably corrects for the fact that the mean fledging time lags behind the inflection point in the proportion of sampled juveniles, all the more so as laying date variance and reproductive success are high. Selection for earlier breeding systematically biases the estimates of mean laying dates, but we show how this bias can be corrected if information on selection strength is available. Multi-site analyses reveal that low sampling effort and high within-site variation can introduce further biases, but also suggest that four sampling sessions with reasonable effort per year provides reasonable estimates. 4. These findings underscore the importance of explicitly modeling the population processes (including possibly selection), and carefully planning sampling designs, to improve the accuracy of phenological estimates from population monitorings, and better interpret climate-driven changes in wild populations.

Community risk from natural hazards and climate change depends largely on physical and climatic settings of an area, socio-economic condition of a community and the magnitude, duration and consecutiveness of the hazard or change itself. Impacts of climate change can be characterized by increasing temperatures, rainfall, humidity changes and climate related extreme events such as floods, cyclone, droughts, sea level rise, salinity and soil erosion etc. Humidity affects crops through evaporation, transpiration and condensation. Crop agriculture is highly influenced by climatic change and majority of population is dependent on agricultural crop in Bangladesh. Any unfavorable change in future climate could have a devastating impact on agriculture and the economy of the country. It is needed to know the socio-economic settings of the rural community, their agricultural practices, anticipated changes in climatic parameters and the link between the climatic variables and crop growth and productivity. Time Series analysis and forecasting has become a major tools in different applications in meteorological phenomena, such as rainfall, humidity, temperature, draught etc. and environmental management fields. Among the most effective approaches for analyzing time series data is the Autoregressive Integrated Moving Average (ARIMA) model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build seasonal ARIMA model for monthly Humidity data taken from Bogra, Dinajpur, Rajshahi and Rangpur stations over the period January, 2001 to October, 2014. In this study, ARIMA (2,0,2)(2,1,2)12, ARIMA (0,1,2)(1,1,1)12, ARIMA (1,0,2)(2,1,1)12 and ARIMA (1,0,2)(2,1,2)12 model respectively are found to be suitable models for Dinajpur, Rajshahi, Bogra and Rangpur stations respectively and these models are used to forecasting the monthly humidity for the upcoming two years to help decision makers to establish priorities in terms of water demand management.

Aim To examine how butterfly population trends respond to climate change and urbanisation at a continental scale, and whether responses differ between urban and rural environments. Location 869 sites across 12 European countries, spanning six bioclimatic zones. Time Period 1976–2021. Major Taxa Studied Butterflies (Lepidoptera). Methods We analysed long‐term monitoring data from > 8400 populations of 145 species representing a wide range of ecological and life‐history traits. Population trends were modelled in relation to climate variables (temperature, precipitation and aridity), urbanisation (built‐up surface), and their interactions with urban context (urban vs. rural) and species traits (trophic specialisation, body size, reproductive rate and thermal adaptation). Results Climate warming and aridification were consistently linked to population declines in both rural and urban contexts, while precipitation effects varied by location and species. Urbanisation alone did not predict trends, but the urban–rural context strongly modulated species' responses to warming, indicating potential synergies between climate change and urbanisation. The stronger impact of warming in urban populations likely reflects elevated baseline temperatures and reduced habitat suitability and connectivity in urban landscapes, limiting thermal buffering. Species with colder thermal niches and lower reproductive rates were most vulnerable to warming, as warming exceeds the thermal optima of cold‐adapted species and lower reproductive rates limit their capacity to buffer climate‐driven population declines. Under aridification, which can reduce host‐plant availability, trophic specialists declined more in urban areas, whereas generalists unexpectedly declined more in rural sites, suggesting context‐dependent constraints under increasing water limitation. Main Conclusions Our findings highlight the complex interplay between climate change, urban context, and species traits in driving population dynamics. Importantly, our results suggest that urbanisation generally amplifies the negative impact of climate change on insect population trends.

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R2 = 0.87) and artificial neural networks (ANNs) performed best for EFR (R2 = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R2 ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change.

Although scientists have warned of possible social perils resulting from climate change, the impacts of long-term climate change on social unrest and population collapse have not been quantitatively investigated. In this study, high-resolution paleo-climatic data have been used to explore at a macroscale the effects of climate change on the outbreak of war and population decline in the preindustrial era. We show that long-term fluctuations of war frequency and population changes followed the cycles of temperature change. Further analyses show that cooling impeded agricultural production, which brought about a series of serious social problems, including price inflation, then successively war outbreak, famine, and population decline successively. The findings suggest that worldwide and synchronistic war-peace, population, and price cycles in recent centuries have been driven mainly by long-term climate change. The findings also imply that social mechanisms that might mitigate the impact of climate change were not significantly effective during the study period. Climate change may thus have played a more important role and imposed a wider ranging effect on human civilization than has so far been suggested. Findings of this research may lend an additional dimension to the classic concepts of Malthusianism and Darwinism.

Human-induced environmental and climate change are widely blamed for causing rapid global biodiversity loss, but direct estimation of the proportion of biodiversity lost at local or regional scales are still infrequent. This prevents us from quantifying the main and interactive effects of anthropogenic environmental and climate change on species loss. Here, we demonstrate that the estimated proportion of species loss of 252 key protected vertebrate species at a county level of China during the past half century was 27.2% for all taxa, 47.7% for mammals, 28.8% for amphibians and reptiles and 19.8% for birds. Both human population increase and species richness showed significant positive correlations with species loss of all taxa combined, mammals, birds, and amphibians and reptiles. Temperature increase was positively correlated with all-taxa and bird species loss. Precipitation increase was negatively correlated with species loss of birds. Human population change and species richness showed more significant interactions with the other correlates of species loss. High species richness regions had higher species loss under the drivers of human environmental and climate change than low-richness regions. Consequently, ongoing human environmental and climate changes are expected to perpetuate more negative effects on the survival of key vertebrate species, particularly in high-biodiversity regions.

Managing the risks posed by climate change and extreme weather to energy production and delivery is a challenge to communities worldwide. As climate conditions change, populations will shift, and demand will re-locate; and networked infrastructures will evolve to accommodate new load centres, and, hopefully, minimize vulnerability to natural disaster. Climate effects such as sea level rise, increased frequency and intensity of natural disasters, force populations to move locations. Displaced population creates new demand for built infrastructure that in turn generates new economic activity that attracts new workers and associated households to the new locations. Infrastructures and their interdependencies will change in reaction to climate drivers as the networks expand into new population areas and as portions of the networks are abandoned as people leave. Thus, infrastructures will evolve to accommodate new load centres while some parts of the network are underused, and these changes will create emerging vulnerabilities. Forecasting the location of these vulnerabilities by combining climate predictions and agent based population movement models shows promise for defining these future population distributions and changes in coastal infrastructure configurations. By combining climate and weather data, engineering algorithms and social theory it has been only recently possible to examine electricity demand response to increased climactic temperatures, population relocation in response to extreme cyclonic events, consequent net population changes and new regional patterns in electricity demand. These emerging results suggest a research agenda of coupling these disparate modelling approaches to understand the implications of climate change for protecting the nation’s critical infrastructure.