Fossil fuel depletion and socio-economic scenarios: An integrated approach
Introduction
In recent years, concerns about the depletion of energy and materials (e.g. peak oil), as well as limits to the ecosystem's assimilation capacity of residues (e.g. climatic change) have been raised in the social, political and business arena. All fossil fuels and minerals are finite and non-renewable on a human scale. These resources are thus limited physically and, more stringently, economically. However, different views about this phenomenon exist in the scientific discussion, opposing “geologists” (or pessimists) vs. “conventional economists” (or optimists). The first [67] argue that geological factors determine a peak in the extraction of each resource that technology can only slightly modify – see for example [22], [81], [128] and the activity of ASPO in http://www.peakoil.net and point out that these restrictions might have strong economic consequences [19], [56], [61], [99], [136]. However, the “conventional economists”, applying the basis of neoclassical growth theory [127], claim that market mechanisms and human ingenuity will be able to both transform resources into reserves and find alternative energy sources to replace the scarce ones at a sufficient pace to avoid supply restrictions, and thus, not affect GDP growth [1], [87], [104], [122], [132]. This paper intends to shed light on this discussion by using a System Dynamics (SD) model that includes both the physical data of the energy resources and the economic data.
The fact that the peak of conventional oil has already occurred has been largely admitted in Academia (e.g. Ref. [100]) as well as by international institutional agencies -together with the acknowledgment of peak oil basic theory as an appropriate methodology – Refs. [13], [147], [150], [148], representing government declarations (e.g. the European Energy Commissioner1 in 2009) and even from some oil companies [97]. In 2012, the ratio of oil in the global energy consumption mix fell to its minimum value in the last 50 years [16]. Annual oil discoveries peaked in the 60s and no oil price rise since then could invert or stop the tendency of declining discoveries thereafter. Due to the close relation between natural gas and oil, the geological understanding of their deposits and depletion is very similar. Conventional wisdom has it that global coal and uranium reserves are ample and supply restrictions due to scarcity must not be expected within the next several decades or even this century, but this is disputed by several studies [35], [40], [60], [96], [115].
Consequently, renewable energy, and particularly solar and wind energy, are the two main sources of renewable energy which might substitute the decline in fossil fuel extraction [124]. However, recent studies of their limits show that their potentials might be even lower than the current final consumption of energy by means of fossil fuels [30], [31], [32]. Thus, if a long-term structural scarcity in energy supplies in the next few years and/or decades occurs, as suggested in the past (e.g. Reports from the Club of Rome [91], [90], Global 2000 [11]), and, more recently [29], [85], [102], [142], this situation would be unprecedented in modern history. Moreover, the study of previous technological transitions shows that they are slow, in the order of decades [49].
On the other hand, energy consumption acts as a climatic change driver [70]. But few studies have focused on the effect of energy constraints in climate scenarios, e.g. Refs. [18], [64], [145].
While depletion estimation for individual fuels following different approaches are relatively abundant (see Refs. [85], [93] for an overview), few studies have centered on the objective of giving a comprehensive study, including estimates for all fossil fuels: Refs. [2], [42], [81], [85], [95], [142] and even fewer have analyzed the whole system and fuel interactions [29], [102], [151], as we propose with our model.
This paper intends to shed light on these issues by describing and showing the results of the model we have developed, WoLiM (World Limit Model), which is a continuation of previous System Dynamic models developed [29], [93]. WoLiM is a structurally-simple and transparent model that compares data from many different sources and helps to view global panoramas. The SD approach allows the combination of different kinds of variables from different knowledge sources, such as socio-economic, geological and technological, so they can be managed and integrated. The model includes the exhaustion patterns of non-renewable resources and their replacement by alternative energies, the estimations of the development and market penetration of alternative technologies, the energy demand of the World's economy under different socio-economic scenarios, the sustainable potential of renewable energies, and the estimations of CO2 emissions related to fossil fuel consumption, all of them viewed in a dynamic framework.
On the other hand, scenario methodology offers an approach to deal with the complexity and uncertainty inherent to these interrelated issues and has become very popular in recent Global Environmental Assessments (GEAs), e.g. IPCC's Assessment reports [69], [70], [75], UNEP's Global Environmental Outlook [140], [138], [137] or the Millennium Ecosystem Assessment (MEA) [89]. Each storyline entails the representation of a plausible and relevant story about how the future might unfold. We judge that this methodology is an adequate one for the design of the socio economic scenarios that are needed as inputs to our model. The paper, therefore, quantifies and implements five representative storylines identified in GEA studies (as described in Ref. [144] and use them as input policies of the WoLiM model. By using this methodology, we replicate the usual visions of the future explored by these international agencies, allowing them to be confronted with the case of the energy development constraints. In fact, to date, these international scientific bodies have largely ignored these constraints [3], [26], [64].2
The paper is organized as follows: Section 2 overviews the model and its main hypothesis and limitations. Section 3 describes the modeling of non renewable and renewable resources. Section 4 explains the estimation of energy demand and Section 5 describes the calculation of CO2 emissions. Scenarios and results are described in Sections 6 Scenarios and policies of the model, 7 Results and discussion. Finally, conclusions are drawn in Section 8.
Section snippets
Overview of the WoLiM model
In recent decades, many global energy-economy-environment models, most focusing on climate change analysis, have been developed (e.g. MESSAGE [101], IMAGE [15], MERGE [86], etc.), some based on system dynamics [12], [28], [46]. However, most of these models tend to use (very) large resource estimates [88] that are subject to high uncertainties and are strongly biased towards overestimation due to the preeminence of optimistic economic assumptions [26], [64], [115]. Thus, few models explicitly
Non-renewable resources
The previous model [93], extensively discusses the different individual fuel extraction profiles proposed in the literature. Thus, we follow their discussion and select the same profiles (updating when new data is available). For some resources, we provide a “Best Guess” and “High Case” estimation based on the literature range (“Best Guess” the one considered most probable and “High Case” the one of highest resources).
Energy demand estimation
A diversity of techniques can be used for estimating the energy demand for an economy or sector. Since the model is highly aggregated, the Energy Intensity method, that has already been used in similar studies [50], [116] has been applied. This method is simplistic because it does not explicitly include the price and the economic structure; however, at medium term, energy demand and its main drivers (GDP and technological improvement) dominate over the variations of fuel prices [29], [50], [116]
CO2 emissions and concentrations
The model computes the CO2 emissions associated with the use of fossil fuels: coefficients from Ref. [16] for conventional and from Refs. [17], [66] for unconventional. Biofuels are far from being neutral carbon emitters due to Indirect land use changes (ILUC); in accordance with Refs. [45], [55], [121], [154] we assign a similar emission power to natural gas (see Table 3).
In order to assess climate change, the net11
Scenarios and policies of the model
As described in Section 2, the WoLiM model needs assumptions about the world socio-economic evolution (such as economic and population growth or technological progress) as external inputs. In order to establish those policies in a coherent and sensible way, we have applied the scenario methodology (e.g. IPCC's Assessment reports [69], [70], [75], UNEP's Global Environmental Outlook [137], [138], [140] or MEA [89]).
Testing system dynamics models and obtaining results from them can be a
Results and discussion
In this section the results of our model to 2050 under the scenarios described in Section 6 are presented. It should be recall that some important issues have not been integrated in the modeling (see Appendix E), most of these issues have the potential to worsen the results obtained.
A fundamental consideration must be made: as the model does not integrate a feedback between energy scarcity and GDP, if the demand cannot be fulfilled, a divergence appears between demand and energy supply (though
Conclusions
In this paper we introduce and apply a System Dynamics model, WoLiM, which aims to fill a gap found among Energy-Economy-Environment models, since few of them integrate the estimations of fossil fuel depletion and alternative energy expectations with the energy demand generated by the socio-economic system. The model is applied to a set of scenarios that replicate the habitual scenarios in Global Environmental Assessment studies [144], IPCC's Assessment reports [69], [70], [75], UNEP's Global
Disclaimer
The opinions expressed in this paper are the authors' own opinions and do not necessarily correspond with those of the Low Carbon Programme.
Acknowledgments
The authors gratefully acknowledge Steve Mohr and Gaetano Maggio for their valuable comments and share of data. This work has been developed within the project CGL2009-14268 funded by the Spanish Ministry of Science and Innovation (MICINN). Additionally, Iñigo Capellán-Pérez wishes to thank the Consejería de Economía y Empleo of la Junta de Castilla y León (Programa de Formación mediante prácticas en materia de investigación e innovación tecnológica) and the REPSOL Foundation for the support
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