Robert Hoffman, past Chair CACOR Board of Directors, considers Canadian Public Policy on Energy and Emissions
It is evident that there has been a failure of public policy in Canada with respect to energy and climate change. The need to reduce greenhouse gas emissions in order to avoid catastrophic global warming was recognized and accepted more than four decades ago based on the scientific consensus that (a) global warming is occurring and (b) it is extremely likely that human-made CO2 emissions have predominantly caused it. Canada was a signatory of the Kyoto Protocol, a treaty adopted in Kyoto, Japan, on December 11, 1997 and entered into force on February 16, 2005. In so doing, Canada committed itself to reductions in greenhouse gas emissions of 6% relative to 1990 levels by 2012. By 2015 emissions were 18% above 1990 levels. Canada withdrew from the Kyoto Protocol effective December 2012. Under the Paris Accord of 2015, Canada committed itself to reducing ghg emissions by 30% of 2005 levels by 2030 and 80% by 2050 in an attempt to keep the increase in global average temperature to well below 2 °C above pre-industrial levels; and to limit the increase to 1.5 °C. By now it is evident that it is highly unlikely that Canada will meet either of these targets
This failure of public policy raises the question of the suitability and adequacy of the process of making public policy with respect to climate change and of the analytical approach and modelling tools that have been brought to bear by the Government of Canada in this domain of public policy. These are the questions addressed in this essay.
Nature of the problem
Meeting aspirations for energy system change and emissions reductions involves a transformation in the structure of the entire economic system. The transition from the existing structure based as it is on fossil fuels to one that is carbon free is apt to occur over a time horizon of 50 to 100 years or more, because the structure of the system is embodied in stocks that take decades to turn over: 15 or 20 years for items such as vehicles and 30 or more years for facilities such as electricity generation and distribution, buildings and infrastructure. The consequence is that the time horizon of the analysis must be long enough to encompass one, if not two, stock roll overs, and that it must focus on pathways or trajectories rather than end points. Further the aspirations for system change over that time horizon are not limited to de-carbonization, but encompass aspirations for economic performance, public health, social well-being, security, biodiversity and resource stewardship.
Energy is an essential component of all Earth system processes. As such it is an integral component of an economy that cannot be treated in isolation from the structure of the whole economic system.
The system within which aspirations are to be met is complex. It has two interacting components: a bio-physical component consisting of a large number of processes that transform materials and energy into the goods providing the services needed to meet human needs, and a cognitive component consisting of large number of economic agents including individuals, households, business enterprises and governments whose decisions and actions establish the structure of economic activity. These disparate processes and agents interact with each other and the relationships among them are non-linear. Understanding complex systems is hard; managing them is even harder.
The future of this complex system is inherently unpredictable as it depends on the accumulation of knowledge and knowhow in the cognitive component that is embodied in materials and energy transformation processes. There are at least two sources of uncertainty that must be recognized. First, new technologies that may emerge are unpredictable and may open up pathways that cannot be anticipated. Unpredictable as well are the responses of agents to circumstances outside the range of past experience.
An Adaptive Approach to Public Policy Development
The long time horizon and the fundamental uncertainties of the problem domain require an adaptive approach to public policy.
The framework for public policy making must encompass elements for reaching consensus on aspirations for system change, for choosing among pathways that can meet those aspirations, and for monitoring the effectiveness of policies intended to put the system onto a selected pathway.
Decision making in matters of public policy is a political process and accountability for decisions of public policy rests with elected officials including ministers and cabinet to whom the public has delegated responsibility for making decisions.
An informed public is essential for effective public policy. There must be a broad-based understanding of how the system works before policies intended to change the behaviour of the stakeholders involved can be implemented, particularly when the interests of the public diverge from those of particular interest groups.
The first element of a public policy framework for making the transition to a low carbon future is setting aspirations for system change, perhaps in the form of targets or goals. The Paris targets are examples of aspirational goals.
Then, it is necessary to identify biophysically and technologically coherent pathways that meet aspirations for system change over a time horizon long enough to see the transition from the current state of the system to a desired state that persists into the future.
Once the ensemble of possibilities has been delineated, each pathway can be assessed and a pathway that best meets societal aspirations can be selected. This is very much a political process. The expression of choice is inevitably subjective; pathways may be valued differently by stakeholders. Choice involves understanding the real trade-offs among interests and negotiating until a choice can be made that is fair and acceptable to all parties.
It is only after a pathway has been selected that policies can be identified and implemented that are intended to change the behaviour of economic agents such that they nudge the system onto the chosen pathway and that milestones along the pathway can be set. A variety of policy instruments are available, ranging from suasion, to taxes, subsidies, trading schemes, regulation, and the establishment of new institutions.
In order to determine whether the selected policy instruments are having their intended effect, the system performance as indicated by measures of the milestone variables along the pathway can be compared with milestones. If the milestones are set for a time period too far into the future, it may not be apparent that the milestones will be missed until it is too late to change course sufficiently for aspirations to be met. If milestones are missed, it will be necessary to adjust pathways and milestones consistent with where the system is at the time of assessment. Milestones may be missed either because the policy instruments did not have the intended effect or because the system was subject to unanticipated disturbances.
When it becomes clear that the system is not on the desired pathway or when the observed values of the milestone variables deviate from the targeted values, policy instruments can be adapted and refined, either by changing the intensity with which they are applied or by introducing new instruments.
At any time, new and unforeseen technologies may open new pathways. Should a new pathway be preferred, new milestones will need to be set and the policy mix will have to be adjusted in such a way that they will steer the system onto the new pathway.
The steps in the framework outlined above are not a one-time sequence. Rather, there is feedback from later to earlier tasks in the sequence as time unfolds.
The adaptive approach to policy making with respect to global warming outlined above contrasts with the current practice that, thus far, has proven to be inadequate.
Ministers of the Crown are advised by expert policy analysts, inevitably economists, who prescribe policies using proprietary economic equilibrium models upon which millions of taxpayer dollars have been spent. Invariably, the policy analysts prescribe a carbon tax as the single most effective tool for reaching targets – in spite of the fact that tax increases are an anathema to the public and the politicians they elect and that the evidence proving that carbon taxes are effective is scant. The policy prescriptions rely on the ability of the analysts to predict the prices of energy carriers over a time horizon of fifty years or more and to understand the response of energy producers and consumers to changes in relative prices.
Furthermore, governments have failed to communicate the understanding of the climate system and the threats to humankind posed by global warming – this, in spite of the overwhelming scientific consensus concerning both the causes and consequences of global warming. As a result, vested interests have been successful in casting doubt on the veracity of the science. This confusion in the public mind has made it easy for elected officials to kick the can down the road and avoid taking actions that might threaten oil and gas interests, the governments that depend on the royalties generated from the production of hydro-carbons, and employment in the oil patch
Governments did agree to emissions targets, but sufficiently far in the future that those who set targets would not be held accountable when they were missed. Further, emissions targets, including those for 2030 and 2050, were set in the absence of the identification and assessment of technologically coherent pathways capable of meeting those targets. Consequently, it has been difficult to assess the effectiveness of policy measures for lack of milestones along pathways against which progress could be evaluated.
Role of Models
With the OPEC initiated energy crisis of 1972, a number of energy models were developed primarily for the purposes of prediction and prescription. For example, energy models were used to project imbalances between supply and demand, to prescribe least cost energy supply options, to predict the impact of oil price increases on energy demand, to produce ‘outlooks’ incorporating trends in the supply and demand of energy, to calculate the greenhouse gas emissions associated with the energy system, and to prescribe energy policies to meet emissions targets.
In the framework for policy analysis outlined above, energy systems models have important roles to play in informing the public, in exploring alternative transition pathways that meet societal aspirations, and in anticipating the impact of policy measures on agents’ behaviour:
- Models augment the capacity of the human mind to understand complex systems that are subject to unpredictable discoveries and evolutionary change.
- They serve to identify the concepts and the relationships among them that are needed to understand and quantify the energy system in the context of national and global economies.
- They make this understanding explicit and communicable, and in so-doing foster a shared understanding within which aspirations for system change can be enunciated.
- Models are needed to explore alternative pathways for meeting societal aspirations and to open up the space of possibilities from which choices must be made
- Models provide quantitative milestones or targets along pathways so that progress in meeting aspirations can be monitored.
- Models can be used to assess the potential effectiveness of alternatives mixes policy measures.
- But, models should not be expected to predict the future of energy systems because of fundamental uncertainties with respect to new knowledge and knowhow and because the future is in part determined by the choices we will make and those choices will be based on what we have yet to learn.,
- Nor can they prescribe best futures because of the lack of objective and agreed upon criteria for determining ‘best’.
Essential feature of models intended to support public policy with respect to energy and emissions
To be effective in playing these roles, energy systems models should be, first of all, transparent. Transparency implies that the values of all variables must be ‘seeable’ in tables and charts for all future scenarios as well as for historical time. Further, the connective structure of the model must be ‘seeable’. It should be possible to trace how changes in one variable impact other variables. The assumptions that underlie future trajectories should be fully documented and accessible.
Models should be accessible to all stakeholders in the decision making process so that stakeholders can explore the consequences of potential actions and understand how the system responds to assumptions with respect policy choices and how sensitive the system is with respect to various parameters. Effectively, the only way to understand complex non-linear systems is to see how they respond to imposed shocks, it being understood that the system will respond differently depending on the state of the system when the shock is applied.
Models should be designed in such a way that stock-flow coherency is assured. The only thing about pathways over a 50 year or longer time horizon that can be said with a high degree of certainty is that they will be coherent with the first and second laws of thermodynamics and the chemistry that governs the transformation of materials and energy. Stock-flow coherency is assured by supply/disposition identities within each time period and stock-flow accounting identities over time. That is to say that the model should assure that the supply of each commodity from production and imports meet requirements for domestic use and exports in each time period and that he stock at the beginning of a time period plus additions to the stock made during the time period less discards from the stock made during the time period must equal the stock at the beginning of the next time period. It is often helpful to keep track of the age structure of stocks as it evolves over time. Stock/flow consistency implies that flows can be accumulated over time and that the model can reflect cumulative effects such as concentrations of greenhouse gases and other pollutants
Models intended to explore alternative pathways should be dynamic – concerned with trajectories or pathways that are anchored in the present and extend in discrete time steps over time horizons long enough to accommodate at least one and preferably two stock roll-overs.
It is necessary to represent the processes that give rise to states of the system as discrete entities. The observed states of a system are a manifestation of the underlying processes. The concept of process is fundamental; it is a dynamic concept concerned with the transformation of time streams of inputs of materials and energy into output streams of products and waste flows within an arbitrary system boundary. Models should represent the array of processes that transform energy sources into energy carriers and that use the exergy in those carriers in the stocks of artifacts that serve to meet human needs for shelter, nutrition, security, mobility, and community. Models should represent processes that could be deployed as well as processes that are currently deployed.
The configuration of processes to be deployed over time is determined by four sets of time-varying parameters: marginal shares that control the process technologies to be added to stocks in each time period, discard parameters that control removals from stocks, input-output parameters that quantify the relationship between inputs and outputs of each process, and intensity parameters that relate stock levels to one another. It is important that all these parameters be user controllable as they are the major determinant of future trajectories.
Calibration is the process of assembling an historical database of all the variables in a model such that all the supply/disposition and stock/flow accounting identities are met. Models should be calibrated in such a way that the settings of the control variables that give rise to observed values of variables over historical time can be seen. This enables the model user to set future values of control variables in the context of past values. As well, calibration provides the vintage structure of the stock variables that are the starting point for future trajectories. Not only does calibration provide a fully integrated historical database, but the ability of a model to run over historical time and produce observed values of output variables is a strong test of model validation.
Canadian Energy Systems Models
There are several sets of energy systems models in Canada that are used for energy and emissions policy analysis.
CIMS, the Canadian Integrated Modelling System, was developed at the Energy and Materials Research Group of Simon Fraser University over the past three decades. CIMS is intended as a tool for energy and emissions policy analysis and supports the policy research program of the Group. Use of CIMS by third parties is commercially supported by Navius Research Inc. CIMS is described as a hybrid model that incorporates bottom-up energy supply and demand modules that are technologically explicit and a top-down macro-economic module. The energy demand module provides the supply module with requirements for energy commodities and in turn the supply module provides energy commodity prices to the demand module until supply and demand are equated. The macro module provides macroeconomic variables to the energy and supply and demand modules. The decisions to deploy technologies are based on consumer choice theory. A model simulation iterates between energy supply and energy demand until energy price changes fall below a threshold value, and repeats this convergence procedure in each subsequent 5-year period of a complete run, which usually extends 30–35 years.
Energy 2020 is a system dynamics model that had its origins in the work of the Dartmouth Systems Dynamics Group as the focus for energy policy was shifting from Washington to the state and company level. In 1985, ENERGY 2020 became the property of Systematic Solutions Inc., an Ohio based company. SSI has performed forecasting, simulation, and policy analysis in over 30 states and provinces in North America as well as state, provincial, and national governments and energy companies in a dozen countries. Both the National Energy Board and Environment and Climate Change Canada commissioned implementations of ENERGY 2020 for Canada. It is often used in conjunction with The Informetrica Model (TIM) or other economy wide models to support the preparation of energy outlooks and policy analyses. It uses consumer choice theory to determine the marginal share coefficients that allocate requirements for additional capacity to available technologies. Producers and consumers choose technologies with the lowest levelized costs where the levelized cost is the present value of initial capital cost and operating costs over the expected useful life of the technology.
NATEM, the North American Times Energy Model, is an implementation of the TIMES linear programming model for North America by ESMIA Consultants for use in the Trottier Energy Futures Project. MARKAL-TIMES is a family of model generators developed by the International Energy Agency and applied in many countries in Europe and throughout the world. MARKAL, originally developed in response to the energy crises of the 1970’s was designed to minimize the cost of meeting requirements for energy commodities. TIMES extends the scope of MARKAL to include the response of energy consumers to changes in the price of energy commodities by maximizing the sum of consumer and producer surplus subject to meeting projected requirements for energy services and subject to emissions constraints. It is effectively a partial equilibrium model in domestic energy commodity markets. The shadow price of the carbon constraint is thought to indicate the price of carbon needed to achieve a target level of emissions.
Both GEEM, General Equilibrium Energy Model, and EC-Pro are computable general equilibrium (CGE) models. GEEM has been Implemented for Canada by Navius Research. EC-Pro has been implemented by ECCC as one of the suite of models for internal departmental use. CGE models are used to simulate how all sectors of the economy may evolve under different economic conditions and to provide insight into how energy and climate policies affect a number of variables, such as: economic activity (GDP), energy consumption, greenhouse gas emissions, trade of goods and services between regions, the competitiveness of different sectors. The fundamental elements of CGE models are an input-output table or social accounting matrix for a single year and a set of elasticities that indicate how markets respond to changes in the costs of inputs and how they respond to changes in demand for the goods and services that they produce – all subject to budgetary constraints.
CanESS, the Canadian Energy Systems Simulator, was developed by whatIf Technologies Inc., an Ottawa based company founded in 1989 to develop custom simulation models using the whatIf suite of modelling tools. CanESS had its origins in the socio-economic resource modelling program at Statistics Canada, the development of energy end-use data bases and custom energy systems models for the National Energy Board, Natural Resources Canada, and Transport Canada. CanESS became operational in 2004 and has been used by clients for a wide range of scenario analyses the most important of which are the Trottier Energy Futures Project and the CESAR Pathways Project at the University of Calgary.
CanESS is designed to simulate stock/flow consistent technology-rich trajectories for the energy and materials transformation processes of Canada and the provinces. In CanESS, the marginal share and life table parameters are the user-supplied control variables that determine the structure of the Canadian economy. In this way the model can be used to explore a wide range of pathways including those that meet targets for greenhouse gas emissions. CanESS represents stocks and flows in physical units, mass, energy or entities, in order to assure coherence with the laws of physics. It represents technologies as discrete processes to assure consistency with the laws of chemical transformations. It does not represent the behaviour of the economic agents who deploy new technologies, as it is the objective of policy makers to change the behaviour of agents in such a way that societal aspirations for economic prosperity, ecological robustness and climate change can be met.
CanESS is designed to make the model user an integral part of an exploratory process rather than an observer of a system in which all the feedbacks are included in the model structure. CanESS is modular. Each module is calculated over the entire time horizon one after another. Downstream modules are allowed to use information calculated upstream, but feedback from downstream to upstream modules is not permitted until after all the modules have been executed and the incoherencies (that result from lack of feedback) are reported. It is then up to the user to reset the control variables in such a way as to resolve incoherencies. A single pass through the sequence of modules is fully deterministic, but the search for coherent solutions is not deterministic because the search involves user input and creativity. CanESS is unique in this respect. Most models resolve incoherencies by incorporating all feedbacks in the model structure by simultaneous equations over all time or by differential equations that assure coherency in each time step before proceeding to next time steps.
The current versions of CanESS focus on the representation of the technologies that are or can be deployed to transform energy from sources (both fossil and renewable) into energy carriers that are used to meet the economies’ needs for mechanical energy, heat, and light. The model accounts for supply of energy from domestic production and imports and the disposition of energy for use in the Canadian economy and for export.
Insofar as technologies are embedded in stocks, the time horizon of the model is distant enough to accommodate at least one and preferably two stock turnovers. Accordingly, CanESS runs in annual time steps over periods of up to 100 years.
How well do the energy systems models serve the adaptive approach to public policy?
Transparency is a challenge for many energy systems models, especially for those that incorporate feedback structures that automatically equate energy supply and demand. Such models are intended to give answers whether the answers are predictions, forecasts or prescriptions. For all intents and purposes they are black boxes – values for exogenous variables are input, the run button is pushed, output is produced in which energy markets are in equilibrium. Results are incorporated in reports or published in professional journals. The causal inference engine that is inside the black box is opaque and inaccessible. It must be taken as an article of faith that what is in the box that links input to output is justifiable and reliable. At best, model documentation may be found that is essentially a list of the equations to be solved often without indicating which variables on the right hand side of equations are calculated on the left hand side of other equations. That the code that solves the equations is an accurate realization of the equations must also be taken as an article of faith. The equation set, whether it takes the form of a linear program or differential equations, implicitly contains the feedback structures needed to assure that the equalities or constraints specified in the equations are met. For partial or general equilibrium models, this means that prices will be found that clear at least the energy commodity markets.
Quantitative causal structures that involve feedbacks – such as the sequence: A influences B, B influences C, and C influences A – are unintuitive and difficult to comprehend for two reasons. First, the idea that effect influences cause is counterintuitive as it is generally perceived that cause precedes effect. Second, non-linearities in the relationship between A and B imply that changes in A will influence B differentially depending on the level of A. Quantitative causal reasoning that involves non-linearities is generally beyond the power of human comprehension. Causality in models that are formulated as sets of simultaneous equations is difficult to understand and such models inherently lack transparency.
It is recognized that transparency is essential for models such as CanESS that are intended to communicate understanding of how the system works and support user-created stock-flow consistent transition pathways. CanESS and the whatIf? platform upon which CanESS is implemented was designed with the objective of transparency in mind.
The block recursive computational structure of CanESS in which modules are executed over all time sequentially according to an a-cyclic dependency structure implies a causal structure without feedbacks. The question ‘what caused this variable to change?’ can always be answered by following the logic of each sub-model in the dependency structure back to the user supplied input that triggered the change and the values of the parameters that have influenced the magnitude of the change.
While the computational structure and user supplied feedbacks make CanESS comprehensible in principle, it is the approach to modelling embodied in the whatIf? suite of modelling software upon which CanESS was built that make it understandable in practice. Model documentation is integral to the modelling process in the whatIf? environment: models are implemented from their documentation. The only way to implement a model is to document it; the only way to change a model is to change its documentation. The first step in model implementation is the creation of a set of influence or design diagrams – one for each sub-model. Each diagram represents the variables, the procedures that effect the transformation of input variables to output variables, and the connective structure among variables and procedures. Sub-models are linked when the outputs of one sub-model are input to other sub-models. Barrels, pipes and hexagons are used to represent stock, flow and parameter variables respectively. Rectangles are used to represent procedures. Solid arrows represent the relational structure and identify inputs into or outputs from procedures. The variables contain information including units of measure and the dimensions over which the variable is defined. Procedures contain the equations or algorithms that effect the transformation in an executable mathematical language that assures the correspondence between the mathematical statement of the model and the code that implements it. These influence diagrams are also used to see the data values for each instance of each variable both in historical time and for future scenarios. The data can be seen as tables, graphs and maps or it can be exported to external software such as spreadsheets or geographical information systems. Scenario data is managed in such a way that the input variables that give rise to particular outputs is available. Ultimately, quantitative causal structures or inference engines that involve non-linear relationships among variables can only be understood by ‘seeing’ how they respond to changes in input variables by means of simulation experiments. This means that open access to models is a key to communicating understanding complex systems.
Accessibility is a problem for all of the Canadian energy systems models. It is perhaps a moot point for models that lack transparency, but accessibility to all stakeholders is essential for models such as CanESS that are intended to communicate understanding and learning by exploration. The whatIf platform assures that models can be accessed from personal computers over the internet such that many users can access a single model simultaneously and scenarios can be shared.
Models including Energy 2020 and EC-Pro are proprietary to the government of Canada. Neither ECCC nor the NEB has the mandate or the resources to support access to all stakeholders.
Models including CIMS, NATEM, GEEM and CanESS are proprietary to for-profit consulting companies and access is limited to those who pay fee-for-service. The consulting companies offer a range of services including conducting one-off analyses, preparing reports based on model output and supporting subscribers who wish to conduct their own analyses.
CanESS is intended to identify physically and technologically coherent pathways that meet aspirations for system change. It does not pre-empt the responsibility of duly constituted political bodies designated responsibility for making choices from amongst alterative pathways. Indeed CanESS is intended to open up the space of possibilities and to include new pathways as new technologies emerge. It is recognized that trade-offs may exist that are best resolved by political processes.
Cost minimization models such as NATEM are intended to find a single ‘best’ or least cost pathway. This focus on identifying a ‘best’ pathway closes the space of possibilities and preempts the political process. The problem is regarded as technical and one that is best resolved by ‘experts’ who assert that values and time preferences can be objectively measured or that they are revealed by market prices.
Models that incorporate consumer choice theory in the representation of the behaviour of producer and consumers, such as Energy 2020 and CIMS limit the choice of biophysically and technologically coherent pathways to those that can be reached by policy-induced changes in agent behaviour. Since agents’ behaviour is usually represented in such a way that agents do not respond to externalities, many options are apt to be missed.
Role of Prices
Models that prescribe policies that are intended to reach distant targets at least cost and those that represent the response of agent behaviour with respect to changes in relative prices require long term projections of prices or they need to represent how prices are set. Prices are needed to calculate future costs. The marginal share parameters are endogenous, ie they are calculated in the model. Usually, one of the variables that contribute to the calculation of the marginal share parameters is the levelized cost, where levelized costs are the present value (at the time the capacity is to be deployed) of the costs over the life cycle of each technology. As such, levelized costs for each technology are a function of the overnight capital cost of the stock, the annual fuel and operating costs, the discount rate, and the capacity utilization rates (capacity factors) over the life of the facility. Not only is it difficult if not impossible to predict prices, but it is important to view prices (that can be influenced by taxes and subsidies) as policy instruments that can be used to put the system on a desired pathway.
In CanESS marginal share parameters are user controlled; hence CanESS does not rely price projections. Pathways can be assessed using multiple criteria including cost and other factors involving externalities and other unintended consequences.
Most, if not all energy systems models for Canada, assure stock/flow coherency for energy feedstocks and carriers in the energy sector and they rely on linkage to a macro-economic model to assure consistency between the energy system and the macro-economy.. But macro-economic models fail to assure stock/flow coherency because, for the most part, flows and stocks in the macro economy are measured in value units, usually constant dollars, that are poor representations of the underlying physical flows.
An important aspect of the supply and disposition of electricity is that supply must equal demand at each moment in time. Electricity demand has significant time-of-day, day-of-the-week, and seasonal variations. Electricity generation facilities vary by ramp-up speed and their capacity may depend on wind speed, hours of sunlight, tidal patterns and water flowrates. Energy systems with increased penetration of intermittent renewables, solar, wind and marine require peaking capacity with high ramp-up speeds or storage capacity.
CanESS is the only model that includes a full 8760 hourly dispatch model using a simulation approach. Annual electricity demand is allocated over the 8760 hours in each year using load shapes associated with detailed sectoral end-uses and installed capacity is assigned for the 8760 hours. Hourly demand is further subdivided into demand that can be satisfied by base-load generating capacity and that which must be satisfied by load –following capacity. Some generating capacity can be (exogenously) designated ‘must-run’ including nuclear, hydro and wind generation capacity. In each hour, given the availability of intermittent energy sources (sun, wind, tides, river-flow) must-run electricity generation is subtracted from hourly demand and the difference is dispatched to base load capacity and load-following capacity according to (exogenous) rank orders of preference specific to baseload and load-following demands. In any given hour, it is possible that there is not enough generating capacity to meet the demand and it is possible that must-run generation exceeds demand; the dispatch algorithm allows for both possibilities.
As indicated above, it is intended that CanESS be extended to include full stock-flow accounting in physical units for materials and material transformations as well as energy.
CanESS is fully calibrated over the period 1978 to 2016: the result is a coherent and fully articulated data base for all the variables, flows, stocks and parameters in CanESS. This is easily the richest energy data base in Canada that integrates data from a wide variety of data sources. In fact calibration is an integral component of the CanESS environment providing historical context for stock variables and parameter values.
It is difficult if not impossible to calibrate optimizing models such as NATEM as that would imply the existence of a single controller who was able to optimize system performance over historical time. It is in principle true that constraints could be added that would limit the historical pathway to track on observed values, but such constraints would defeat the purpose of searching for a least cost pathway.
Models that include representations of agent behaviour are difficult to calibrate because, agent behaviour, in reality, is far too complex to be fully captured in equations whose parameters are estimated from aggregate time series data. CanESS does not ‘explain’ the observed variation in the historical values of the control variables; rather CanESS displays that variation so that the user can see the variability in deciding how to set future values.
Conclusions and recommendations
- To be effective, a framework for public policy making with respect to energy and emissions must encompass elements for reaching consensus on aspirations for system change, for choosing among physically coherent pathways that can meet those aspirations, and for monitoring the effectiveness of policies intended to put the system onto a selected pathway. An informed public is critical.
- The process must be supported with modelling tools, analyses, and data accessible to all stakeholders. These tools should be designed in such a way that they can be used to explore alternative pathways and policies that meet aspirations for system change. They should not purport to predict the future nor to prescribe ‘best’ policies under the assumption that there are objective criteria for assessing ‘best’.
- The existing stock of energy systems models in government, academia and business enterprises are not adequate for sound policymaking and public understanding of energy and its interaction with the economy and the environment: they are inaccessible; they lack transparency; they are too narrowly focused; they pre-empt the role of those delegated responsibility for making decisions; most are underfunded.
- There is an urgent and pressing need in Canada for the establishment of an arms-length-from-government, not-for-profit publicly funded institution with a mandate to develop and support public access to energy systems models for Canada and their underlying data bases so as to promote sound policy making and public understanding of energy and its interaction with the economy and the environment.
- From the discussion above, it is clear that the CanESS model, with its emphasis on stock-flow consistency and the explicit representation of the technological processes that transform materials and energy, both extant and prospective, is uniquely well positioned for exploring alternative pathways that meet emissions targets and other aspirations for system restructuring. The institution proposed above should fund the development of CanESS-like models to encompass material as well as energy transformations and support access to and the use of CanESS-like models by all stakeholders.
- Robert Hoffman is Principal and Founder of whatIf? Technologies Inc., and a member of both the Canadian Association for the Club of Rome and the international Club of Rome.
- Information and documentation for Energy 2020 may be found here.
- Information about CanESS may be found at http://www.whatiftechnologies.com/caness.
- An overview of the TIMES (The Integrated MARKAL-EFOM System) model generator upon which the NATEM was based may be found here: https://iea-etsap.org/index.php/etsap-tools/model-generators/times
- Information about Navius Research and the GEEMS model may be found here: https://www.naviusresearch.com/gtech/. Navius has consolidated CIMS, GEEM and a pipelines model called Oiltrans into a single model called gTech.
- The final report of the Trottier Energy Futures Project entitled, Canada’s Challenges and Opportunity: Transformations for major reductions in GHG emissions, may be found here: https://www.cae-acg.ca/wp-content/uploads/2013/04/3_TEFP_Final-Report_160425.pdf
Hoffman, Robert. (2016). “Towards a Conceptual System for Managing in the Anthropocene”. Cadmus Journal, Volume 3, Issue 1, October 2016, pp 149-158.
Hoffman, Robert and McInnis, Bert. (2015) “Concepts for a New Generation of Global Modelling Tools: Expanding our Capacity for Perception”. Cadmus Journal Vol 2, Issue 5, pp 134-145, October 2015.
Hoffman, Robert. (2012). “On the Need for New Economic Foundations: A Critique on Mainstream Macroeconomics”. Cadmus Journal Vol 1, Issue 5, Part 2, pp 74-85, October 2012.
Jaccard, Mark. (2009). Combining top down and bottom up in energy economy models. In International Handbook on the Economics of Energy edited by Joanne Evans and Lester C Hunt. Cheltenham: Edward Elgar Publishing.
Rivers, Nic and Jaccard, Mark. (2006). Useful Models for simulating technological Change. Energy Policy 34 (2006) 2038 – 2047.
Turner,G.M.; Hoffman, Robert; McInnis, Bertram; Poldy Franzi & Foran, Barney. (2011). “A tool for strategic biophysical assessment of a national economy: The Australian stocks and flows framework”. Environmental Modelling & Software 26 (2011) 1134 – 1149.
Gault, F.D., K.E. Hamilton, R.B. Hoffman, and B.C. McInnis. (1987). “The Design Approach to Socio-Economic Modelling”, Futures, 19 (1), 3-25.
Rivers, N. and M. Jaccard (2005), ‘Combining top-down and bottom-up approaches to energy–economy modelling using discrete choice methods’, The Energy Journal, 26(1), 83–106.