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. 1. THE oRiGinAL BoTToM-uP / ToP-DoWn DiviSion Policy-makers are interested in a better understanding of the effectiveness and cost of policies whose purpose is to shift energy systems toward more environmentally desirable technology paths. What technologies would serve this purpose, and how could or would the economy adapt in response to policy to
Achieving fast and inclusive economic growth concurrently with greenhouse gases (GHG) emission control could have wide-ranging implications for the Indian economy, predominantly fuelled by fossil energies. India faces high income inequality with the bottom 50% of its population owning only 2% 2 of total national wealth. Other developmental challenges include 304 million people living in poverty, 269 million without access to electricity, 92 million without access to safe drinking water, and around 2 million homeless. Despite such challenges, India has committed to reduce the GHG emission intensity of its GDP 33% to 35% below its 2005 level by 2030, including via turning 40% of its power-generation capacity away from fossil sources. To explore the macroeconomic consequences of achieving development along low-carbon pathways, we use a hybrid modelling architecture that combines the strengths of the AIM/Enduse bottom-up model of Indian energy systems and the IMACLIM top-down economy-wide model of India. This hybrid architecture stands upon an original dataset that reconciles national accounting, energy balance and energy price statistics. With this tool, we demonstrate that low-carbon scenarios can accommodate yearly economic growth of 5.8% from 2013 to 2050 i.e. perform close to if not slightly higher than our business-as-usual scenario, despite high investment costs. This result partly stems from improvement of the Indian trade balance via substantial reduction of large fossil fuel imports.Additionally, it is the consequence of significant shifts of sectoral activity and household consumption towards low-carbon products and services of higher value-added. These transitions would require policies to reconcile the conflicting interests of entrenched businesses in retreating sectors like coal and oil, and the emerging low-carbon sectors and technologies such as renewables, smart grids, electric vehicles, modern biomass energy, solar cooking, carbon capture and storage, etc.
Household lifestyles, and activity patterns in particular, greatly influence household energy use. In this paper we analyse the disparities in current activity patterns and related energy consumptions and expenditures of households, for a comprehensive set of everyday activities covering 24 hours. Thanks to detailed data on energy consumption by end use, we are able to allocate the total of household energy consumptions to the appropriate activities. We comment on average energy and expenditure intensities of time uses of the total population as well as of income, household-composition and housing-type subgroups. Income, an obvious driver of energy and expenditure intensities, is revealed to influence time use as well. Household composition and housing type are also associated with substantial variations in activity patterns and in the energy and expenditure intensities of activities, even within a given income group. Indeed, sometimes the variations associated with income are smaller than the variations associated with other variables. We therefore underline the importance of household disaggregation in household energy analyses, to properly account for such disparities.
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