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Prospective Outlook on Long-term Energy Systems (POLES) is a world simulation model for the energy sector that runs on the Vensim software. It is a techno-economic model with endogenous projection of energy prices, a complete accounting of energy demand and supply of numerous energy vectors and associated technologies, and a carbon dioxide and other greenhouse gases emissions module.
POLES was initially developed in the early 1990s in the Institute of Energy Policy and Economics IEPE (now EDDEN-CNRS) in Grenoble, France. It was conceived on the basis of research issues related to global energy supply and climate change and the long-term impact of energy policies. It was initially developed through a detailed description of sectoral energy demand, electricity capacity planning and fossil fuel exploration and production in the different world regions. Along its development it incorporated theoretical and practical expertise in many fields such as mathematics, economics, engineering, energy analysis, international trade and technical change.
The initial development of POLES was financed by the JOULE II and III programmes of the European Commission’s Third and Fourth Framework Programmes (FP) for Research and Technological Development (1990-1994 and 1994-1998) as well as by the French CNRS. Since then, the model has been developed extensively through several projects, some partly financed by FP5, FP6 and FP7, and in collaboration between the EDDEN-CNRS, the consulting company Enerdata and the European Joint Research Centre IPTS.
With a history spanning twenty years, it is one of the few energy models worldwide, that has been in continuous development process and over such an extended time period.
The model provides a complete system for the simulation and economic analysis of the world’s energy sector up to 2050. POLES is a partial equilibrium model with a yearly recursive simulation process with a combination of price-induced behavioural equations and a cost- and performance-based system for a large number of energy or energy-related technologies. Contrary to several other energy sector models, international energy prices are endogenous. The main exogenous variables are the gross domestic product and population for each country or region.[1]
The model’s structure corresponds to a system of interconnected modules and articulates three levels of analysis: international energy markets, regional energy balances, and national energy demand (which includes new technologies, electricity production, primary energy production systems and sectoral greenhouse gas emissions).
POLES breaks down the world into 66 regions, of which 54 correspond to countries (including the 28 countries of the European Union) and 12 correspond to countries aggregates; for each of these regions, a full energy balance is modelled. The model covers 15 energy demand sectors in each region.
Each demand sector is described with a high degree of detail, including activity indicators, short- and long-term energy prices and associated elasticities and technological evolution trends (thus including the dynamic cumulative processes associated with technological learning curves). This allows a strong economic consistency in the adjustment of supply and demand by region, as relative price changes at a sectoral level impact all key component of a region’s sector. Sectoral value added is simulated.
Energy demand for each fuel in a sector follows a market share-based competition driven by energy prices and factors related to policy or development assumptions.
The model is composed of the following demand sectors:
There are 88 oil and gas production regions with inter-regional trade; these producing regions supply the international energy markets, which in turn feed the demand of the 66 aforementioned world regions. Fossil fuel supply modelisation includes a technological improvement in the oil recovery rate, a linkage between new discoveries and cumulative drilling and a feedback of the reserves/production ratio on the oil price. OPEC and non-OPEC production is differentiated. The model includes non-conventional oil resources such as oil shales and tar sands.
There are 30 electricity generation technologies, among which several technologies that are still marginal or planned, such as thermal production with carbon capture and storage or new nuclear designs. Price-induced diffusion tools such as feed-in tariffs can be included as drivers for projecting the future development of new energy technologies.
The model distinguishes four typical daily load curves in a year, with two-hour steps. The load curves are met by a generation mix given by a merit order that is based on marginal costs of operation, maintenance and annualized capital costs. Expected power demand over the year influences investment decisions for new capacity planning in the next step.
The model includes accounting of greenhouse gas (GHG) emissions and allows visualising GHG flows on sectoral, regional and global levels. POLES covers fuel combustion-related emissions in all demand sectors, thus covering over half of global GHG emissions. The six Kyoto Protocol GHGs are covered (carbon dioxide, methane, nitrous oxide, sulphur hexafluoride, hydrofluorocarbons and perfluorocarbons).
The model can be used to test the sensibility of the energy sector to the carbon price as applied to the price of fossil fuels on a regional level, as envisaged or experimented by cap and trade systems like the EU’s Emissions Trading Scheme.
The model’s databases have been developed by IPTS, EDDEN and Enerdata. Data on technological costs and performances were provided by the TECHPOL database.[2][3] The data for historical energy demand, consumption and prices are compiled and provided by Enerdata.[4]
The POLES model can be used to study or test the effect of different energy resources assumptions or energy policies and assess the importance of various driving variables behind energy demand and the penetration rates of certain electricity generation or end-use technologies. POLES does not directly provide the macro-economic impact of mitigation solutions as envisaged by the Stern Review, however it allows a detailed assessment of the costs associated with the development of low- or zero-carbon technologies.
Linked with GHG emissions profiles, the model can produce marginal abatement cost curves (MACCs) for each region and sector at a desired time; these can be used to quantify the costs related to GHG emissions reduction or as an analysis tool for strategic areas for emissions control policies and emissions trading systems under different market configurations and trading rules.[5][6]
Studies including POLES simulations have been commissioned by international bodies such as several Directorates-General of the European Commission,[7][8] national energy, environment, industry and transport agencies[9] or private actors in the energy sector.[10][11] [12]
POLES can model changes in sectoral value added and shifts of activity between sectors. However POLES is not a macroeconomic model in the sense that it uses the gross domestic product as an input and includes no feedback on it that could result from the evolution of the energy system: carbon pricing, falling oil production and its effect on transport and mobility, or growth induced by technological innovation (such as the IT boom of the 1990s). As such, it does not provide the total impact on society of, e.g., climate adaptation or mitigation (it does however quantify the total cost to the energy sector, including investment necessary in the development of low-carbon technologies).
The model does not cover all greenhouse gases emissions, notably those related to agriculture (in part), land use, land-use change and forestry. As such, the climate component of the model does not allow to fully project GHG stocks, concentrations and associated temperature rises from anthropogenic climate change.[13]
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Prospective Outlook on Long-term Energy Systems (POLES) is a world simulation model for the energy sector that runs on the Vensim software. It is a techno-economic model with endogenous projection of energy prices, a complete accounting of energy demand and supply of numerous energy vectors and associated technologies, and a carbon dioxide and other greenhouse gases emissions module.
An electric car is an automobile that uses electrical energy as the primary source of propulsion. The term normally refers to a plug-in electric vehicle, typically a battery electric vehicle (BEV), which only uses energy stored in on-board battery packs, but broadly may also include plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REEV) and fuel cell electric vehicle (FCEV), which can convert electric power from other fuels via a generator or a fuel cell. Compared with conventional internal combustion engine (ICE) vehicles, battery electric cars are quieter, more responsive and more energy-efficient, produce no exhaust emissions, and generally have lower life-cycle greenhouse gas emissions; the size of the climate benefit depends on factors including the vehicle, electricity mix, manufacturing and lifetime use. Due to the superior efficiency of electric motors, electric cars also generate less waste heat, thus reducing the need for engine cooling systems that are often large, complicated and maintenance-prone in ICE vehicles. The battery of an electric car is typically recharged by connecting it to an external electricity supply. Recharging an electric car can be done at different kinds of charging stations; these charging stations can be installed in private homes, parking garages and public areas. There is also research and development in, as well as deployment of, other technologies such as battery swapping and inductive charging. As recharging centers (especially fast chargers) are widespread in some areas, range anxiety and the time required to charge are psychological obstacles for some consumers considering the purchase of an electric car. Worldwide, 21 million plug-in electric cars were sold in 2025, accounting for over a quarter of new car sales and representing a 20% increase from 2024. Many countries have established government incentives for plug-in electric vehicles, including tax credits, subsidies, and other non-monetary incentives while several countries have legislated to phase out sales of fossil fuel cars, to reduce air pollution and limit climate change. China currently has the largest stock of electric vehicles in the world, with cumulative sales of 22.09 million units through December 2024, although these figures also include heavy-duty commercial vehicles such as buses, garbage trucks and sanitation vehicles, and only account for vehicles manufactured in China. As of 2020, the total cost of ownership of recent electric vehicles in the United States and European Union was lower than that of equivalent ICE cars, due to lower power and maintenance costs. In 2023, the Tesla Model Y became the world's best-selling car. The Tesla Model 3 became the world's all-time best-selling electric car in early 2020, and in June 2021 it became the first electric car to pass 1 million global sales.
Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research. This multifaceted research domain necessitates the collaborative efforts of chemists, biologists, mathematicians, physicists, and engineers to decipher the biology of intricate living systems by merging various quantitative molecular measurements with carefully constructed mathematical models. It represents a comprehensive method for comprehending the complex relationships within biological systems. In contrast to conventional biological studies that typically center on isolated elements, systems biology seeks to combine different biological data to create models that illustrate and elucidate the dynamic interactions within a system. This methodology is essential for understanding the complex networks of genes, proteins, and metabolites that influence cellular activities and the traits of organisms. One of the aims of systems biology is to model and discover emergent properties, of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. By exploring how function emerges from dynamic interactions, systems biology bridges the gaps that exist between molecules and physiological processes. As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although it is consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "the reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer et al.) "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." (Denis Noble) As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, multiomics (transcriptomics, metabolomics, proteomics, etc) and high-throughput techniques are used to collect quantitative data for the construction and validation of models. A comprehensive systems biology approach necessitates: (i) a thorough characterization of an organism concerning its molecular components, the interactions among these molecules, and how these interactions contribute to cellular functions; (ii) a detailed spatio-temporal molecular characterization of a cell (for example, component dynamics, compartmentalization, and vesicle transport); and (iii) an extensive systems analysis of the cell's 'molecular response' to both external and internal perturbations. Furthermore, the data from (i) and (ii) should be synthesized into mathematical models to test knowledge by generating predictions (hypotheses), uncovering new biological mechanisms, assessing the system's behavior derived from (iii), and ultimately formulating rational strategies for controlling and manipulating cells. To tackle these challenges, systems biology must incorporate methods and approaches from various disciplines that have not traditionally interfaced with one another. The emergence of multi-omics technologies has transformed systems biology by providing extensive datasets that cover different biological layers, including genomics, transcriptomics, proteomics, and metabolomics. These technologies enable the large-scale measurement of biomolecules, leading to a more profound comprehension of biological processes and interactions. Increasingly, methods such as network analysis, machine learning, and pathway enrichment are utilized to integrate and interpret multi-omics data, thereby improving our understanding of biological functions and disease mechanisms.
Energy modeling or energy system modeling is the process of building computer models of energy systems in order to analyze them. Such models often employ scenario analysis to investigate different assumptions about the technical and economic conditions at play. Outputs may include the system feasibility, greenhouse gas emissions, cumulative financial costs, natural resource use, and energy efficiency of the system under investigation. A wide range of techniques are employed, ranging from broadly economic to broadly engineering. Mathematical optimization is often used to determine the least-cost in some sense. Models can be international, regional, national, municipal, or stand-alone in scope. Governments maintain national energy models for energy policy development. Energy models are usually intended to contribute variously to system operations, engineering design, or energy policy development. This page concentrates on policy models. Individual building energy simulations are explicitly excluded, although they too are sometimes called energy models. IPCC-style integrated assessment models, which also contain a representation of the world energy system and are used to examine global transformation pathways through to 2050 or 2100 are not considered here in detail. Energy modeling has increased in importance as the need for climate change mitigation has grown in importance. The energy supply sector is the largest contributor to global greenhouse gas emissions. The IPCC reports that climate change mitigation will require a fundamental transformation of the energy supply system, including the substitution of unabated (not captured by CCS) fossil fuel conversion technologies by low-GHG alternatives.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker
