Cognition, Vol. 9, No. 1

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Such simplifications opening up previously inaccessible areas, fragmenting the might negatively influence public support and consumer landscape, and pressurizing the water supplies 7. Addition- behavior, which in turn might influence investors and policy ally, oil extraction activities have been linked to increased makers. What is more, holding only part of a sector wildlife and wild meat trade, further affecting biodiversity bioenergy accountable for the negative consequences of 8.

It therefore makes sense to expand our viewpoint of LUC will disturb a level playing field for the renewable energy promoting the most beneficial biofuels only from an sector and affect the transition from fossil resources. This might result in conditions are eligible for future exploitation. The de- approach the problem of LUC with integration, coherence, velopment of a climate-friendly energy policy framework and systemic thinking.

Next to cutting down GHG emissions, requires estimation and regulation of the combined impacts, society needs to prioritize the protection of areas of important direct and indirect effects, of all fuel pathways This value for biodiversity, ecosystem resilience, and ecosystem means that GHG emissions associated with LUC impacts of services to all kinds of exploitation. Smart land use manage- fossil exploration and exploitation need to be included and ment based on the precautionary principle is a vital part of accounted for in order to implement life cycle emission sustaining and maintaining the life support mechanism of regulations.

Good practice therefore would advocate re- our planet.

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An integrated approach to the problem of LUC searching these as well, especially in cases where surface asks for a systems level redesign of our socioecological regime mining is involved; for example, oil shale, tar sands, and and economic system 33 in a way that it sustains, instead deep sea oil exploration and drilling. The importance of of reduces, the life support mechanism of the planet. It indirect effects has been illustrated by the recent paper of suggests new institutional and organizational arrangements Liska and Perrin , who estimated that the protection interlinking a range of topics and policies previously ad- of oil supplies in the Middle East by the US military measured dressed independently: economy, energy, climate mitigation as indirect military emissions could raise the GHG intensity and adaptation, agriculture, land use and management, of gasoline from this source by roughly 2-fold.

Using natural resources, environment, poverty, development aid, petroleum-based resources as a reference energy system for health, and others On the individual level, it calls for a comparisons with bioenergy consequently necessitates a re-evaluation of our priorities. Country level biodiversity is represented by an index based on species diversity in the four terrestrial vertebrate classes and vascular plants using national biodiversity indices. The smaller map represents high priority global biodiversity areas and zero extinction sites dots.

Without such changes, economic and individual interests will dominate the decisions that affect LUC. The 1 Intergovernmental Panel on Climate Change. Leen Gorissen is a biologist with a Ph. Nature , , ecology, evolution, and behaviour. Her research focuses on systemic — Energy Information Administration. Annual Energy Outlook biodiversity, climate change, and transition management. Veerle investigates the sustainability 4 Unconventional Fossil-Based Fuels. Luc Desc.

  • New Energy Architecture.
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Resources and future supply of oil. Bioenergy Task Please address correspondence regarding this article to leen. Oil and Gas projects in the Western Amazon: Threats to wilder- ness, biodiversity and indigenous peoples. PLoS One , 3, Acknowledgments e We thank E. Lambin, F. Nevens, L. Blyth, and K. Schoeters 8 Suarez, E.

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  6. Avail- Anim. Greenhouse gas implica- 25 Dew, J. Road to tions of land use change and land conversion to biofuel crops. Island Press: New York, ; pp Ecosystems and Bilsborrow, R. Ecuadorian Amazon. World Dev. The landscape approach: designing new Monitoring impact of hydrocarbon exploration in sensitive reserves for protection of biological and cultural diversity in terrestrial ecosystems: perspectives from Block 78 in Peru. In Latin America. Ethics , 30, — Footprints in the Jungle; Bowles, I. Unconventional Oil. The causes of land-use and land-cover change: moving Scraping the Bottom of the Barrel?

    Global Environ. Change , 11, — Trends Ecol. Carbon footprint of nations: A 30 Appendix E: Threatened and Endangered Species within the global, trade-linked analysis. Programmatic Envi- — Bureau of Land Manage- 16 Loorbach, D. Netherlands, Science , , — Overshoot, adapt and recover. The pathways for the growth in energy use were plotted to show non-renewable fuel depletion trajectories at varying rates of GDP 3. The Breusch-Godfrey Serial Correlation Lagrange multiplier test [ 32 , 33 ], see Table B in S1 File , was performed to demonstrate that the model does not suffer from any serial dependence between data points.

    The Durbin—Watson statistic [ 34 , 35 ] see Table B in S1 File additionally shows that this model does not suffer from serial correlation given the time lag components of the model.

    How Well Does Fossil Fuel Divestment Combat Climate Change?

    These tests which are fully described in the S1 File , demonstrate that this dynamic model does not suffer from serial correlation effects. Furthermore, to test for stationarity properties of the time series used in this study we have used the Augmented Dickey-Fuller ADF test [ 36 , 37 ]. As shown in Table B to G in S1 File , the null and alternative hypothesis can be rejected given the t-statistic values being smaller than the required critical values prescribed by Mackinnon [ 38 , 39 ]. These tests show that the explanatory variables of the model are both necessary and sufficient to describe the growth of energy use.

    Unconventional Fossil-Based Fuels: Economic and Environmental Trade-Offs

    Using data from to , the observed relationship between global energy demand Fig 1A , global gross domestic product and global population was modelled. This model was specified in growth rates annual first differences of the natural logarithms of variables plus the natural logarithms of the levels of variables. The model is dynamic and allows for lags in impact. The structure of this model is reported in Eq 1 with the lags denoted in parenthesis: 1. The chosen base year for global energy use is 0.

    The model passes a stringent battery of statistical and econometric tests see S1 File.

    The model captures the relationship between global energy demand, global GDP and global population over recent history. A tight correlation is observed Fig 1. There is a large literature on the direction of causation between energy and GDP which is inconclusive and this is unsurprising given their intimate connection [ 40 ].

    Here we make no assumption concerning the direction of causality since it is not required for the forecasting exercises undertaken. Our aim was to establish for a given GDP growth rate, what the correlated energy demand growth is forecasted to be, irrespective of direction of causation.

    Under different conditions either energy availability e. Widely accepted global projections for population growth have been used, and GDP per capita rates are based on historical ranges [ 27 , 28 ]. Forecasting scenarios are presented for different assumptions concerning global population growth and economic growth.

    Unconventional fossil-based fuels : economic and environmental trade-offs

    Future risks to energy security and CO 2 emissions targets were then evaluated Fig 2. The independent variables specified as rates of change capture short term impacts and those in levels capture long term impacts. Population growth increases the demand for energy sharply in the short term; however this is moderated by falling energy use per capita See Fig 1A and Fig 1B. When we restrict them to be equal by entering lnEnergy t-1 —lnGDP t-1 as a single independent variable, the reported result is very similar to an estimated coefficient of What this suggests is that, as energy has been used more efficiently over time, energy demand has grown in response.

    International Energy Agency modelling of energy demand assumes that energy use is highly correlated with the raw measure of economic activity GDP [ 12 ]. At the global level it is therefore often assumed that, over time, energy efficiency improvements in production contributing to GDP, will broadly be achieved in most economic sectors and that this will enable global energy demand to be controlled. This improvement in energy efficiency was largely achieved through increasing knowledge and innovation which has driven technological energy efficiency [ 42 ].

    However in accordance with the above Jevons paradox example, rather than reducing energy consumption per person ZJ Person -1 , individuals globally have used 2. The historical energy use data Fig 1B shows that improvements in energy efficiency, raise growth in energy use. It is also the case that energy demand increases significantly faster than population in the short term because the damping effect of energy efficiency operates over a longer time lag.

    Thus an increase in energy efficiency associated with GDP Fig 1B red is also offset by population growth effects which are associated with the second part of the Jevons paradox. It should be emphasised that this additional energy use is not "discretionary" in the usual sense of the word. It reflects the fact that a higher standard of living intrinsically requires more energy via underlying production, regulation and standards, redundancy and range of services.

    While some energy is "wasted" by consumers, most of this additional consumption is due to structural changes that cannot be removed without a discernible downgrading of quality of life e. In summary, although production efficiency per unit of GDP level has increased, each person uses more energy at the same time as global population rises. Thus, the potential for rapidly increasing energy demand in the future is high as global population is conservatively estimated to increase towards the widely predicted 9 billion by and possibly beyond [ 42 ].

    As most governments promote economic growth, poverty alleviation and increased energy equality, using only ZJ GDP -1 is inadequate. Eq 1 accounts for both simultaneously see also Fig 1A. Fig 1A charts actual and predicted global energy use time series data from — The approximately linear trend was likely due to a relatively stable fraction of the global population i. The rest of the world used a much smaller fraction. From to however, the economies of China Fig 1A clearly shows that the trend has begun to steepen since Fig 1C also shows that GDP growth is quite volatile.

    Large short-term increases in energy prices, as in and , reduce planned production with consequent falls in GDP and the consumption of energy. This short run effect is confirmed in Eq 1 and results in an estimated coefficient on GDP growth in excess of unity, indicating energy use is highly influenced by changes in economic conditions when all other variables remain constant.