Futures of global urban expansion: uncertainties and implications for biodiversity conservation

Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km ^2 circa 2000 to 1440 000 ± 65 000 km ^2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km ^2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km ^2 , is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies

Correlation of urban built form, density and energy performance

In order to optimize the energy consumption in cities and enhance the potential of using renewable energy sources, the form of the city is considered as an influential factor. Numerous indicators have been used to analyse the effect of density and other characteristics of urban form on energy use. The paper presents results of an investigation into the relationships of building energy performance with two important urban density indicators, namely site coverage and volume-area ratio. Generic mathematical model of pavilion urban built form has been developed in order to compare and contrast its land-use/density characteristics with energy performance. Energy analysis has been performed on geometrical models using urban simulation software. The relationship between energy and density indicators are compared by considering an important variables, namely plan depth, cut-off angle and number of storeys. The city of London, representing a temperate climate, is considered as a case study. According to the results, high-rise buildings with deeper plans achieve higher energy efficiency. However, in case of including PV energy generation, low-rise buildings with deeper plans illustrate better total energy performance. Graphical results provide urban planning guidelines that can be used by urban designers, planners and architects to facilitate the most energy-efficient built form density for promoting more sustainable cities

A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies

Scenarios that limit global warming to 1.5 °C describe major transformations in energy supply and ever-rising energy demand. Here, we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the global North and global South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today, despite rises in population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation. Our scenario meets the 1.5 °C climate target as well as many sustainable development goals, without relying on negative emission technologies

Shady business: understanding the spatial ecology of exophilic Anopheles mosquitoes

Background: Understanding the ecology of exophilic anophelines is a key step toward developing outdoor control strategies to complement existing indoor control tools against malaria vectors. This study was conducted to assess the movement pattern of exophilic Anopheles mosquitoes between blood meal sources and resting habitats, and the landscape factors dictating their resting habitat choice. Results: Resting clay pots were placed at 5 m, 25 m, 50 m, 75 m and 100 m away from isolated focal houses, radiating from them in four directions. The locations of the clay pots represent heterogeneous land cover types at a relatively fine spatial scale in the landscape. The effect of the landscape characters on the number of both female and male anophelines caught was modelled using zero-inflated negative binomial regression with a log link function. A total of 420 Anopheles mosquitoes (353 females and 67 males) belonging to three species; Anopheles arabiensis, Anopheles pharoensis, and Anopheles tenebrosus were caught in the resting clay pots, with An. arabiensis being the dominant species. Canopy cover, distance from the house, and land cover type were the significant landscape characters influencing the aggregation of resting mosquitoes. Both the count and binary models showed that canopy cover was the strongest predictor variable on the counts and the presence of Anopheles mosquitoes in the clay pots. Female Anopheles were most frequently found resting in the pots placed in banana plantations, and at sampling points that were at the greater distances (75 m and 100 m) from the focal house. Conclusions: This study showed that exophilic Anopheles mosquitoes tend to rest in shaded areas some distance away from human habitation. These findings are important when targeting mosquitoes outdoors, complementing the existing effort being made to control malaria vectors indoors

Comparative Coastal Risk Index (CCRI): A multidisciplinary risk index for Latin America and the Caribbean

As the world's population grows to a projected 11.2 billion by 2100, the number of people living in low-lying areas exposed to coastal hazards is projected to increase. Critical infrastructure and valuable assets continue to be placed in vulnerable areas, and in recent years, millions of people have been displaced by natural hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Risks related to natural hazards are determined by a complex interaction between physical hazards, the vulnerability of a society or social-ecological system and its exposure to such hazards. Moreover, these risks are amplified by challenging socioeconomic dynamics, including poorly planned urban development, income inequality, and poverty. This study employs a combination of machine learning clustering techniques (Self Organizing Maps and K-Means) and a spatial index, to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The proposed method meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets, effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index. Our results demonstrate that in LAC, more than 500,000 people live in areas where coastal hazards, exposure (of people, assets and ecosystems) and poverty converge, creating the ideal conditions for a perfect storm. Hotspot locations of coastal risk, identified by the proposed Comparative Coastal Risk Index (CCRI), contain more than 300,00 people and include: El Oro, Ecuador; Sinaloa, Mexico; Usulutan, El Salvador; and Chiapas, Mexico. Our results provide important insights into potential adaptation alternatives that could reduce the impacts of future hazards. Effective adaptation options must not only focus on developing coastal defenses, but also on improving practices and policies related to urban development, agricultural land use, and conservation, as well as ameliorating socioeconomic conditions

Climate change effects on people’s livelihood

Generally climate is defined as the long-term average weather conditions of a particular place, region, or the world. Key climate variables include surface conditions such as temperature, precipitation, and wind. The Intergovernmental Panel on Climate Change (IPCC) broadly defined climate change as any change in the state of climate which persists for extended periods, usually for decades or longer (Allwood et al. 2014). Climate change may occur due to nature’s both internal and external processes. External process involves anthropogenic emission of greenhouse gases to the atmosphere, and volcanic eruptions. The United Nations Framework Convention on Climate Change (UNFCCC) made a distinction between climate change attributable to human contribution to atmospheric composition and natural climate variability. In its Article 1, the UNFCCC defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” (United Nations 1992, p. 7)

Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%–357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%–37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries

Resilience, social-ecological rules, and environmental variability in a two-species artisanal fishery

Social-ecological resilience is an increasingly central paradigm for understanding sustainable resource management. In this study, we aimed to better understand the effect of environmental variability on the resilience of fishery systems, and the important role that social institutions and biophysical constraints play. To explore these issues, we built a dynamic model of the pen shell fishery of the indigenous Seri people in the Gulf of California, Mexico. This model included the dynamics of the two dominant species in the fishery (Atrina tuberculosa and Pinna rugosa), several institutional rules that the Seri use, and a number of ecological constraints, including key stochastic variables derived from empirical data. We found that modeling with multiple species, rather than the standard one-species model, uncovered more of the resilience that is present in the system. We also found that it is the combination of several social-ecological rules working in conjunction with the endogenous environmental variability that helps ensure the resilience of the system. © 2013 by the author(s)