Recent geopolitical issues have brought energy and food security to the forefront of the political debate. When assessing the self-sufficiency of a region and its dependence on external sources, the availability of a reliable information system becomes crucial for informed decision-making. In this context, this study presents a complexity-based approach, namely the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM), to examine the interdependence between exosomatic metabolism (linked to energy security) and endosomatic metabolism (associated with food security) with respect to externalised processes. The epistemological tool of the metabolic processor is used to bridge different pieces of information together (from the microscale of the technical conversions to the macroscale of the whole country) to provide a comprehensive view of countries’ metabolism. To accomplish this, it is essential to establish a transparent framework of assumptions that encompasses different scales, resource types (such as primary flows vs secondary inputs), and nexus elements (such as water as a flow element and land as a fund element). The proposed approach applies to Andorra, a small country in the European orbit. Results indicate that Andorra exhibits a high degree of dependence on other territories. Regarding energy imports, Andorra externalises seven times the working hours, 40 times the land, and six times the water compared to its local energy sector. Moreover, externalised GHG emissions are also 17 times higher than its local energy sector. Considering its endosomatic metabolism, Andorra relies on eight times more human activity, 15 times more land, and 22 times more blue water (with seven times the GHG emissions) through food imports compared to its local agricultural system. These findings also raise essential considerations regarding decarbonising policies and ethical concerns. They prompt us to question whether we are genuinely improving the system’s performance or merely shifting problems elsewhere. Furthermore, they raise concerns about the displacement of environmental pressures and social conflicts beyond governance boundaries.

The role of energy in economic production and its contribution to economic development is subject to ongoing debate. Here, we address this issue by carrying out a stochastic frontier analysis of the EU15 countries, considering both non-quality and quality correct production factors and especially considering models with and without the contribution of energy, where quality-corrected energy is obtained through the innovative useful exergy metric. This latter metric is set obtained at the useful, where energy is being in fact used for the creation of economic value (in contrast to the conventionally considered primary and final stages of energy transformation), and sums up correctly all forms of energy, namely by quantitatively recognizing that heat flows have a lower value than work flows.
We find that, as expected both from mainstream economic theory (regarding capital and labour) and from ecological economics theory (regarding energy) all results obtained are economically more meaningful when considering quality corrected production factors.
We find that the output elasticities of energy are increasing along time, even overtaking those of labour. Across countries, the output elasticity of useful exergy is roughly constant (in contrast to those of labour and of capital), confirming its stable and hence close connection to the creation of economic value. More developed countries have both lower TFP growth rates, lower returns to scale and higher persistent efficiency (as would be expected in more developed countries).

For policy consideration, the result implies that given that the growth of TFP is slowing down as a country becomes more developed, it becomes important to focus on making better use of production inputs, with an emphasis on improving input quality. In terms of energy, a higher final-to-useful exergy efficiency should be pursued.

To comply with the CO₂ emission targets there is an urgent need to transition from fossil energy sources in different sectors of the economy, especially in land transport where currently oil products are widely used. The replacement of fossil fuels is not the first energy transition the transport sector has experienced. The history of land transport can offer valuable insights for policymakers to shape future energy transitions by identifying hidden patterns in past transitions, opportunities, and threats. There is a complex interplay between the evolution of fuel consumption, energy efficiency, energy service, and CO₂ emissions that requires further exploration. A global database covering energy use in all stages and CO₂-eq emissions of animal, rail, and motorized road transportation, as well as the number of draft animals, length of rail track, energy service on railways, and the road vehicles in use from 1800 to 2021 was developed. To deal with missing data, machine learning and backcasting techniques were implemented. It was the first time that machine learning was used in a historical energy reconstruction study. The results obtained reveal that for the world land transport: (1) animal transport has been using more final energy than railways since the 1980s, (2) the peak in final energy use on railways occurred in the 1940s (10536 PJ), while useful energy use has not yet peaked, (3) road primary, final, and useful energy use has been growing since 1910 with an average growth rate of around 7% per year, however, the final-to-useful efficiency has been limited by pollutants regulations, (4) land transport’s primary, final, and useful energy use increased 68, 107, and 340-fold between 1800 and 2015, whereas final-to-useful efficiency tripled, and (5) electric trains are currently the most sustainable mode of transport emitting around 0.18 kg of CO₂ per useful MJ, while steam trains are the least sustainable ones with 1.6 kg of CO₂ per useful MJ. Furthermore, insights identifying key points of land transport to be addressed in future policies are proposed.

Information and communication technologies (ICT) are increasingly used in every aspect of our life and are expected to continue to do so, hence making fundamental the study of the energy associated to the production and usage of ICT devices. Here we focus on electricity consumption during usage. Historically, electricity usage of ICT has increased, both in absolute terms and as a fraction of total electricity usage, reaching close to 7% of global electricity demand by 2020.

However, there is great uncertainty over the range of future electricity consumption of ICT, one of the mains factors being the efficiency of ICT devices, which increased significantly, by several orders of magnitude, in the past, but might reach its technological limits (minimum energy needed associated with the current technology) in the coming decades. The doubt about how close we are to specific limits is associated to the difficulty in measuring energy efficiency in ICT devices, since, unlike other devices, which are energy to energy converters, ICT devices are energy to information converters.

To analyse this, we focused on ICT's computing component (personal computers, servers, data centres, smartphones, supercomputers) and examined the use phase of electricity consumption, efficiency and information processing over the history of digital computing, since the mid-1970s. Our approach to quantifying electricity consumption, efficiency, and processed information involved an analysis encompassing the cumulative quantity of computing devices across diverse classifications (personal computers, servers, etc.) and their corresponding performance metrics, power utilization, and operational hours.

We found that since the mid-1970s energy efficiency has improved by 8 orders of magnitude. In this period, we found a surge of 13 orders of magnitude in the total annual processed information while electricity consumption increased five orders of magnitude. The increasing use of mobile computing devices, such as laptops and smartphones, is having a positive impact on aggregate energy efficiency, leading to reduced energy consumption in computing activities. However, the anticipated rise in the adoption of cloud computing is expected to increase the information processed by data centers, which will likely lead to higher energy consumption in these facilities.

Overall, in a re-enactment of the rebound effect, we can see that past improvements in efficiency failed to reduce electricity usage, casting doubt on the potential for future efficiency to do so, unless specific policies are enacted with this aim in mind.

Energy careers dynamics are often modeled using a sigmoid, or S-curve, the solution of the Verhulst equation with constant coefficients. It is the case for example for oil (with the Hubbert curve) or with photovoltaic (PV) and wind energy. Such approach suggests that production is self-developing, independently of any external control, what is contrary to the nature of such dynamics as it is driven by investments from the energy industry itself.

In this study, it is suggested to couple production with EroEI to highlight the role of investment in the development dynamic of energy careers, using a first principle derived model and the definition of EroEI at point of use.

This coupling allows to distinguish two specific kind of dynamics: the one associated with fossil fuels and the one associated with low carbon emission careers such as PV and wind. It highlights the limits of fossil fuel, extraction through the study of the investment required to keep the business as usual of these careers, which is straightforwardly depending on the EroEI evolution.

It also allows to study the limit of low carbon emission careers dynamic, showing how fast PV and wind industries can actually grow. Therefore, this method allows to study how fast low carbon emission careers can substitute fossil fuels, considering different energy scenarios, from business as usual to degrowth. In the transition process, the calculation of the “energy for energy” is straightforwardly calculated, providing the physical bound of the energy transition.

Besides, the methodology allows to calculate a dimensionless index that reflect the ability of a given energy career to grow, providing an objective metric to compare the different low carbon emission careers and their ability to efficiently handle energy transition.