Developing an Input-Output Based Method to Estimate a National-Level Energy Return on Investment (EROI)

Brand-Correa, Lina I.; Brockway, Paul E.; Copeland, Claire; Foxon, Timothy J.; Owen, Anne; Taylor, Peter

doi:10.3390/en10040534

Cited by 31 publications

(15 citation statements)

References 62 publications

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“…The concept of EROI is part of the field of net energy analysis (NEA), and is one way of measuring and comparing the net energy availability from different energy sources and processes. In general, EROI can be defined as "the ratio between the energy returned from an energy-gathering activity compared to the embodied energy in that process" [55]. Although this concept was used initially to develop an energy-focused approach to the economy [56], the concept was adapted to calculate ratios between food energy output and food production energy inputs.…”

Section: Energy Return On Investment (Eroi)mentioning

confidence: 99%

Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology

et al. 2018

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Feeding the world’s population sustainably is a major challenge of our society, and was stated as one of the key priorities for development cooperation by the European Union (EU) policy framework on food security. However, with the current trend of natural resource exploitation, food systems consume around 30% of final energy use, generating up to 30% of greenhouse gas (GHG) emissions. Given the expected increase of global population (nine billion people by 2050) and the amount of food losses and waste generated (one-third of global food production), improving the efficiency of food systems along the supply chain is essential to ensure food security. This study combines life-cycle assessment (LCA) and data envelopment analysis (DEA) to assess the efficiency of Spanish agri-food system and to propose improvement actions in order to reduce energy usage and GHG emissions. An average energy saving of approximately 70% is estimated for the Spanish agri-food system in order to be efficient. This study highlights the importance of the DEA method as a tool for energy optimization, identifying efficient and inefficient food systems. This approach could be adopted by administrations, policy-makers, and producers as a helpful instrument to support decision-making and improve the sustainability of agri-food systems.

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Section: Energy Return On Investment (Eroi)mentioning

confidence: 99%

Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology

et al. 2018

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“…The IEA documentation states that 'Energy industry own use contains the primary and secondary energy consumed by transformation industries for heating, pumping, traction, and lighting purposes, (including) for example, own use of energy in coal mines, own consumption in power plants and energy used for oil and gas extraction.' Some studies have used the EIOU to calculate EROI at a national level (e.g., Brand-Correa et al [114], King et al [115]), but the IEA metric only partly reflects the boundaries usually adopted for the EROI denominator [116]. In 2015, the IEA reported EIOU as 8.9% of total final global consumption ( [117], p. 47).…”

Section: A Proposed Net-energy Feedback Modelmentioning

confidence: 99%

A Biophysical Perspective of IPCC Integrated Energy Modelling

Palmer¹

2018

Energies

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The following article conducts an analysis of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), specifically in relation to Integrated Assessment Models (IAMs). We focus on the key drivers of economic growth, how these are derived and whether IAMs properly reflect the underlying biophysical systems. Since baseline IAM scenarios project a three-to eight-fold increase in gross domestic product (GDP)-per-capita by 2100, but with consumption losses of only between 3-11%, strong mitigation seems compatible with economic growth. However, since long-term productivity and economic growth are uncertain, they are included as exogenous parameters in IAM scenarios. The biophysical economics perspective is that GDP and productivity growth are in fact emergent parameters from the economic-biophysical system. If future energy systems were to possess worse biophysical performance characteristics, we would expect lower productivity and economic growth, and therefore, the price of reaching emission targets may be significantly costlier than projected. Here, we show that IAMs insufficiently describe the energy-economy nexus and propose that those key parameters are integrated as feedbacks with the use of environmentally-extended input-output analysis (EEIOA). Further work is required to build a framework that can supplement and support IAM analysis to improve biophysical rigour.

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“…In 1984, Cleveland et al [4] noted that the EROI value for a given energy source must be greater than 1:1 for its use to make sense in an economy; they also noted that fuels (or energy carriers) with higher EROI values play a greater economic role than do fuels (or energy carriers) with lower EROI values because of the importance of net fuel supply. Brand-Correa et al [5] calculated Some of these studies focused on the EROI ST analysis, while others studied a specific oil field or project. All these studies concluded that the EROI ST of China's fossil fuels is low and decreasing compared to those of most other nations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Point-of-Use Energy Return on Investment and Net Energy Yields from China’s Conventional Fossil Fuels

Feng

Wang

2018

Energies

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There is a strong correlation between net energy yield (NEY) and energy return on investment (EROI). Although a few studies have researched the EROI at the extraction level in China, none have calculated the EROI at the point of use (EROI POU ). EROI POU includes the entire energy conversion chain from extraction to point of use. To more comprehensively measure changes in the EROI POU for China's conventional fossil fuels, a "bottom-up" model to calculate EROI POU was improved by extending the conventional calculation boundary from the wellhead to the point of use. To predict trends in the EROI POU of fossil fuels in China, a dynamic function of the EROI was then used to projections future EROI POU in this study. Results of this paper show that the EROI POU of both coal (range of value: 14:1-9.2:1), oil (range of value: 8:1-3.5:1) and natural gas (range of value: 6.5:1-3.5:1) display downward trends during the next 15 years. Based on the results, the trends in the EROI POU of China's conventional fossil fuels will rapidly decrease in the future indicating that it is more difficult to obtain NEY from China's conventional fossil fuels.

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Developing an Input-Output Based Method to Estimate a National-Level Energy Return on Investment (EROI)

Cited by 31 publications

References 62 publications

Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology

Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology

A Biophysical Perspective of IPCC Integrated Energy Modelling

Analysis of Point-of-Use Energy Return on Investment and Net Energy Yields from China’s Conventional Fossil Fuels

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