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Although economists prefer a unique, economy-wide carbon price, climate policies are likely to continue to combine technology- and sector-specific regulations with, at best, some degree of carbon pricing. A hybrid energy-economy model that combines technological details with partial macro-economic feedbacks offers a means of estimating the likely effects of this kind of policy mix, especially under different scenarios of technological innovation. We applied such a model, called CIMS-US, in a model comparison project directed by the Energy Modeling Forum at Stanford University (EMF 24) and present here the interrelated effects of policies focused separately on electricity and transportation. We find that technological innovation encouraged by transportation regulation can inadvertently increase emissions from electricity generation and ethanol production to the extent that abatement from the regulation itself is effectively neutralized. When, however, regulation of electricity generation is combined with transportation policy or there is economy-wide carbon pricing, substantial abatement occurs.

Government, utility, and private subsidies for energy efficiency play a prominent role in current efforts to reduce greenhouse gas emissions, yet the effectiveness of this policy approach is in dispute. One opportunity for empirical analysis is provided by the past energy efficiency subsidies, called demand-side management programs, offered by electric utilities in North America over several decades. Between 1990 and 2005, most electric utilities in Canada administered such programs, with total spending of $2.9 billion (CDN$2005). This paper uses the significant inter-annual variation in demand side management spending during this period to econometrically estimate the effectiveness of these subsidies. The resulting estimates indicate that these programs have not had a substantial impact on overall electricity consumption in Canada.doi: 10.5547/ISSN0195-6574-EJ-Vol32-No4-5

Analysts using a bottom-up approach have argued that a large potential exists for improving energy efficiency profitably or at a low cost, while top-down modelers tend to find that it is more expensive to meet energy conservation and greenhouse gas (GHG) reduction goals. Hybrid energy-economy models have been developed that combine characteristics of these divergent approaches in order to help resolve disputes about costs, and test a range of policy approaches. Ideally, such models are technologically explicit, take into account the behavior of businesses and consumers, and incorporate macroeconomic feedbacks. In this study, we use a hybrid model to simulate the impact of end-use energy efficiency standards and an economy-wide carbon tax on GHG emissions and energy consumption in the U.S. to the year 2050. Our results indicate that policies must target abatement opportunities beyond end-use energy efficiency in order to achieve deep GHG emissions reductions in a cost-effective manner.doi: 10.5547/ISSN0195-6574-EJ-Vol32-SI1-4

As an energy-economics modeler, who collaborated with academics while also consulting to government and industry, Campbell Watkins was especially interested in the empirical relationship between energy inputs and economic output. His skills were perfectly suited to this pressing research issue, which first emerged in the mid-1970s as the “energy-capital substitution” controversy. As his publication record shows, he worked with leading researchers in the development and econometric testing of dynamic specifications of this relationship. But he conducted this work always with a concern for how the research might be useful for immediate policy decisions. Today, the key policy question is the extent to which humanity can reduce its energy-related greenhouse gas emissions at reasonable cost. A new generation of “hybrid, top-down/bottom-up” models attempts to address the objectives Campbell listed in his widely circulated 1992 book chapter, particularly his point that technological change should not be treated as completely exogenous, but at least in part as a very long-run response to price changes and policies. But while current energy models are increasingly constructed to incorporate this feedback effect- notably those models used for simulating climate policies - the empirical estimation of their key parameters is still in its infancy. As Campbell noted in his characteristic dry humor, the scope for research remains “undiminished.” More hard-nosed researcher-practitioners like Campbell would certainly help.

Hybrid energy–economy models combine the advantages of a technologically explicit bottom–up model with the behavioral realism sought after by top–down models in order to help policymakers assess the likely technology-specific response and economy-wide impact of policies to induce technological change. We use a discrete choice survey to estimate key technology choice parameters for a hybrid model. Two choice experiments are conducted for household energy-related decisions about retrofitting home building structures and choosing a space heating and conditioning system. Based on a discrete choice survey of 625 householders, we estimate a discrete choice model and then demonstrate how its parameters translate into the behavioral parameters of a hybrid model. We then simulate household energy policies, including, individual subsidies and increased regulations.Hybrid energy–economy models combine the advantages of a technologically explicit bottom–up model with the behavioral realism sought after by top–down models in order to help policymakers assess the likely technology-specific response and economy-wide impact of policies to induce technological change. We use a discrete choice survey to estimate key technology choice parameters for a hybrid model. Two choice experiments are conducted for household energy-related decisions about retrofitting home building structures and choosing a space heating and conditioning system. Based on a discrete choice survey of 625 householders, we estimate a discrete choice model and then demonstrate how its parameters translate into the behavioral parameters of a hybrid model. We then simulate household energy policies, including, individual subsidies and increased regulations.

Most energy-economy policy models offered to policy makers are deficient in terms of at least one of technological explicitness, microeconomic realism, or macroeconomic completeness. We herein describe CIMS, a model which starts with the technological explicitness of the “bottom-up” approach and adds the microeconomic realism and macroeconomic completeness of the “topdown” CGE approach. This paper demonstrates CIMS’ direct utility for policy analysis, and also how it can be used to better estimate the long run capital-forenergy substitution elasticity (ESUB) and autonomous energy efficiency index (AEEI) technology parameters used in top-down models. By running CIMS under several possible energy price futures and observing their effects on capital and energy input shares and energy consumption, we estimate an economy-wide ESUB of 0.26 and an AEEI of 0.57%, with significant sectoral differences for both parameters.

After nearly two decades of debate and fundamental disagreement, top-down and bottom-up energy-economy modelers, sometimes referred to as modeling ‘tribes’, began to engage in productive dialogue in the mid-1990s (IPCC 2001). From this methodological conversation have emerged modeling approaches that offer a hybrid of the two perspectives. Yet, while individual publications over the past decade have described efforts at hybrid modeling, there has not as yet been a systematic assessment of their prospects and challenges. To this end, several research teams that explore hybrid modeling held a workshop in Paris on April 20-21, 2005 to share and compare the strategies and techniques that each has applied to the development of hybrid modeling. This special issue provides the results of the workshop and of follow-up efforts between different researchers to exchange ideas.

Recently, hybrid models of the energy-economy have been developed with the objective of combining the strengths of the traditional top-down and bottom-up approaches by simulating consumer and firm behavior at the technological level. We explore here the application of discrete choice research and modeling to the empirical estimation of key behavioral parameters representing technology choice in hybrid models. We estimate a discrete choice model of the industrial steam generation technology decision from a survey of 259 industrial firms in Canada. The results provide behavioral parameters for the CIMS energy-economy model. We then conduct a policy analysis and show the relative effects of an information program, technology subsidy, and carbon dioxide tax on the uptake of alternative industrial steam generation technologies, including boilers and cogeneration systems. We also show how empirically derived estimates of parameter uncertainty can be propagated through the model to provide uncertainty estimates for major model outputs.

Interest groups and experts debate the cost of greenhouse gas (GHG) reduction, and policy-makers do not know whom to believe. The confusion stems from differing definitions of costs and divergent assumptions about key uncertainties, especially the role of policy in influencing the long-run evolution of technologies and consumer preferences. Analysis could be more helpful to policy-makers by combining technological explicitness with behavioral realism in hybrid models. With such a model, we demonstrate how GHG reduction cost estimates vary depending on whether the analyst focuses just on the financial costs of technologies or combines this with other relevant components of consumer and business preferences, such as option value and consumers’ surplus. We also show how this type of model can allow policy-makers to explore the uncertain relationship between policies and the evolution of technologies and preferences, which are critical factors in the long-run cost dynamics of GHG emission reduction. We explore these generic methodological issues with a case study of GHG reduction costs in Canada.

A significant share of future urban energy consumption is predetermined when land-use and urban form are designated. Community Energy Management (CEM) is a sustainable energy strategy which looks at how shaping the built environment and designing urban services in consideration of energy production, distribution and use could affect both the long term demand for energy and the type of energy supplied. This energy policy perspective is particularly relevant to China as that country is experiencing rapid urbanisation and significant urban air quality issues.A spreadsheet model is used to evaluate aggregate energy-related emissions in the year 2015 from two alternative scenarios of urban growth throughout China. The model focuses on how energy demand, residential energy technology penetration and transportation mode choices are affected by factors of density and mix of use in neighbourhood development. Results from this exercise suggest that China can achieve urban residential and transportation emission reductions of approximately 14% for CO2, 10% for SO2, 40% for NOXand 14% for particulate emissions in 2015 by adopting certain aspects of CEM. Issues around the implementation of CEM are also addressed in this study by examining key institutional and policy issues involved in land-use planning, site and building design, alternative energy supply and transportation management. Recommendations and implementation strategies are suggested.

For integrated resource planning, electric utilities require estimates of the technical and economic conservation potential. This potential depends upon the efficiencies of existing equipment, as well as efficiencies and costs of new equipment. The industrial conservation potential is generally concentrated in machine drive: electric motors and the various auxiliary technologies (pumps, fans, etc.) and process technologies (grinders, saws, etc.) to which they are connected. Most studies of industry focus on the potential due to more efficient motors and electronic adjustable speed drives. Our study of industry in British Columbia extends this analysis in two ways: (1) Alternative configurations and equipment types of key auxiliary and process equipment and connecting mechanisms are included in the database and analysis. (2) The relationship is specified between different auxiliary technologies and major steps in each production process. This allows for a more complete and dynamic estimate of conservation potential, showing how it changes as a function of structural and major process change in industry. The resulting industry-wide estimates of technical and economic conservation potential range from 35% to 40%, in the year 2010, with significant differences between end-uses (15% to 70%) and between industry branches (20% to 42%).

Cost estimates for reducing energy-related CO2 emissions vary with modeling assumptions and methods. Much debate has centered on the tendency for top-down models to suggest high costs and for bottom-up models to suggest low costs. This study incorporates behavioral parameters, derived from end-use equipment acquisition surveys, in a bottom-up simulation model of the residential sector in order to probe the basis for differing cost estimates and to test various policy suggestions. Simulating the effect of carbon taxes on a business as usual forecast, the results suggest that a CO2 tax will lead to significant net costs of adjustment if the factors leading to higher private discount rates reflect in part real costs and risks. The results also suggest that it may be in society's interest to pursue fuel switching policies with equal or greater vigour than energy efficiency improvements for the goal of reducing CO2emissions in the residential sector. As further research helps to distinguish the significance of these perceived costs and risks, and to refine projections of technology costs, the inputs to the model can be adjusted in order to refine the estimates for policy makers of CO2 reduction costs and of appropriate strategies for achieving reduction goals.