@article{khan_inequality_2018,
title = {Inequality and energy: Revisiting the relationship between disparity of income distribution and energy use from a complex systems perspective},
author = {Fouad Khan and Paul Heinecker},
url = {https://www.sciencedirect.com/science/article/pii/S2214629618303074},
doi = {10.1016/j.erss.2018.03.026},
issn = {2214-6296},
year = {2018},
date = {2018-08-01},
urldate = {2018-04-10},
journal = {Energy Research & Social Science},
volume = {42},
pages = {184--192},
abstract = {To consider the impacts of economic inequality on energy consumption efficiency we need indicators that take into account the complexity of the economic and energy systems. We also need decision support tools that help incorporate such indicators into policy analysis. Drawing inspiration from urban studies and ecology, in this paper we develop a scaling indicator for income disparity in national economies that is a measure of system complexity and does not presuppose any distribution as ideal. The scaling indicator is calculated for 2010 income distribution data for countries. We show that rising disparity – measured using this indicator calculated; a) for distributions of incomes across consecutive twentieth percentiles of population in national economies and; b), for distributions of population density in census blocks in metropolitan statistical areas affects energy consumption efficiency in a diametrically different manner in cities and nation states leading to a higher urban carbon footprint while increasing energy efficiency nationally. The different nature of these two systems explains the results. We then modify tools for visualizing complexity from urban studies and ecology to explore the correlation between income disparity and energy efficiency in national economies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{garnett_total_2018,
title = {Total systemic failure?},
author = {Philip Garnett},
url = {https://www.sciencedirect.com/science/article/pii/S0048969718300950},
doi = {10.1016/j.scitotenv.2018.01.075},
issn = {0048-9697},
year = {2018},
date = {2018-06-01},
urldate = {2018-02-24},
journal = {Science of The Total Environment},
volume = {626},
pages = {684--688},
abstract = {While the world argues about whether climate change is real, what if all systems are failing? This paper seeks to ignite further discussion concerning human impact on all aspects of our environment as we move further into the Anthropocene, not only in terms of the pressure we produce, but also how our activity changes the nature of the relationships between Earth's systems. The paper suggests that we currently lack the tools and analytical capacity to understand the significance of these changes and therefore we cannot answer the question, “are all systems failing?”. We discuss how complexity theory, complex networks, and Artificial Intelligence, could contribute part of a solution.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{wachtmeister_oil_2018,
title = {Oil projections in retrospect: Revisions, accuracy and current uncertainty},
author = {Henrik Wachtmeister and Petter Henke and Mikael Höök},
url = {http://www.sciencedirect.com/science/article/pii/S0306261918303428},
doi = {10.1016/j.apenergy.2018.03.013},
issn = {0306-2619},
year = {2018},
date = {2018-06-01},
urldate = {2018-04-05},
journal = {Applied Energy},
volume = {220},
pages = {138--153},
abstract = {Scenarios and projections are important for decision and policy making. Accuracy of past projections can be useful for both scenario users and developers, for insight on current projection uncertainty, and for guiding improvement efforts. This paper compiles projections of oil production, oil prices and upstream investments from the years 2000 to 2016 from the annual World Energy Outlook by the International Energy Agency, and investigates revisions and accuracy of past projections and implied uncertainty of current ones. Revisions of world oil production, price and investments have been motivated by a combination of demand and supply factors. Downward revisions are mainly allocated to OPEC, while recent upward revisions are due to unconventional oil, in particular US tight oil. Non-OPEC conventional projections have been stable. Price and investments have been revised mostly upwards. Projection accuracy follows the size and directions of these revisions, with high accuracy for Non-OPEC (mean absolute percentage error of 4.8% on a 5 year horizon) and low for OPEC (8.9%) and unconventional (37%). Counteracting error directions contribute to accurate total World oil supply projections (4%) while price projections have low accuracy (37%). Scenario users should be aware of implied uncertainty of current oil projections. In planning and decision making, uncertainty ranges such as those presented here can be used as benchmarks. Scenario developers should focus improvements efforts on three areas in particular: tight oil, OPEC and new technology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{susnik_data-driven_2018,
title = {Data-driven quantification of the global water-energy-food system},
author = {Janez Sušnik},
url = {http://linkinghub.elsevier.com/retrieve/pii/S092134491830082X},
doi = {10.1016/j.resconrec.2018.02.023},
issn = {09213449},
year = {2018},
date = {2018-06-01},
urldate = {2018-04-05},
journal = {Resources, Conservation and Recycling},
volume = {133},
pages = {179--190},
abstract = {There is increasing interest in the global water-energy-food (WEF) system and potential system trajectories, especially considering growing concerns over resource exploitation and sustainability. Previous studies investigating different aspects of this system have a number of shortcomings, meaning it is difficult to identify system-wide tradeoffs, and makes comparison difficult. A global analysis of the WEF system linked to gross domestic product (GDP) growth is presented, integrating the four sectors into a coherent analysis and modelling framework. GDP was included as previous related work demonstrates a link between GDP and each WEF sector. A system dynamics modelling approach quantifies previously qualitative descriptions of the global WEF-GDP system, while a Monte-Carlo sampling approach is adopted to characterise national-level variability in resource use. Correlative and causal analysis show links of varying strength between sectors. For example, the GDPelectricity consumption sectors are strongly correlated while food production and electricity consumption are weakly correlated. Causal analysis reveals that ‘correlation does not imply causation’. There are noticeable asymmetries in causality between certain sectors. Historical WEF-GDP values are well recreated. Future scenarios were assessed using seven GDP growth estimates to 2100. Water withdrawals in 2100 and food production in 2050 are close to other estimations. Results suggest that humanity risks exceeding the ‘safe operating space’ for water withdrawal. Reducing water withdrawal while maintaining or increasing food production is critical, and should be decoupled from economic growth. This work provides a quantitative modelling framework to previously qualitative descriptions of the WEF-GDP system, offering a platform on which to build.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{wang_emotions_2018,
title = {Emotions predict policy support: Why it matters how people feel about climate change},
author = {Susie Wang and Zoe Leviston and Mark Hurlstone and Carmen Lawrence and Iain Walker},
url = {http://linkinghub.elsevier.com/retrieve/pii/S095937801731004X},
doi = {10.1016/j.gloenvcha.2018.03.002},
issn = {09593780},
year = {2018},
date = {2018-05-01},
urldate = {2018-03-23},
journal = {Global Environmental Change},
volume = {50},
pages = {25--40},
abstract = {Current research shows that emotions can motivate climate engagement and action, but precisely how has received scant attention. We propose that strong emotional responses to climate change result from perceiving one’s “objects of care” as threatened by climate change, which motivates caring about climate change itself, and in turn predicts behaviour. In two studies, we find that climate scientists (N = 44) experience greater emotional intensity about climate change than do students (N = 94) and the general population (N = 205), and that patterns of emotional responses explain differences in support for climate change policy. Scientists tied their emotional responses to concern about consequences of climate change to future generations and the planet, as well as personal identities associated with responsibility to act. Our findings suggest that “objects of care” that link people to climate change may be crucial to understanding why some people feel more strongly about the issue than others, and how emotions can prompt action.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}