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The industry sector is a major energy consumer and GHG emitter. Effective climate change mitigation strategies will require a significant reduction of industrial emissions. To better understand the variations in the projected industrial pathways for both baseline and mitigation scenarios, we compare key input and structure assumptions used in energy-models in relation to the modeled sectors' mitigation potential. It is shown that although all models show in the short term similar trends in a baseline scenario, where industrial energy demand increases steadily, after 2050 energy demand spans a wide range across the models (between 203 and 451 EJ/yr). In Non-OECD countries, the sectors energy intensity is projected to decline relatively rapidly but in the 2010–2050 period this is offset by economic growth. The ability to switch to alternative fuels to mitigate GHG emissions differs across models with technologically detailed models being less flexible in switching from fossil fuels to electricity. This highlights the importance of understanding economy-wide mitigation responses and costs and is therefore an area for improvements. By looking at the cement sector in more detail, we show that analyzing each industrial sub-sector separately can improve the interpretation and accuracy of outcomes, and provide insights in the feasibility of GHG abatement. [ABSTRACT FROM AUTHOR]

Merging climate change mitigation and sustainable development in developing countries is pivotal for the transition towards low carbon growth pathways. This paper combines the field of technology transfer and technology-specific aspects with sustainable development objectives. The general climate change mitigation paradigm has shifted from project oriented mitigation action to more strategic, country-wide, cross-sectoral mitigation plans, in order to explicitly take into account also economic development goals. Local technology needs and socio-technical circumstances are important towards economic development induced by technology transfer. Yet, this approach is not sufficient for the success of technology transfer, which shall also deliver on economic development. A strategy for the adoption of technologies, as well as the broadening of the domestic technology manufacturing base, needs to consider also the technology properties itself in greater detail. The technology transfer process should emphasize the economic developmental purpose as well as the properties of technologies. Thus, I propose a detailed assessment of the technology and its potential of being adopted by suggesting that technology complexity assessments should be integrated into technology transfer mechanisms. By using CSP, PV and wind technology as examples, I describe how the evaluation of technology complexity and of potential economic development, determined by demand for manufactured goods and services within domestic economies, which could lead to job creation and value added, could be used to inform policy makers. [ABSTRACT FROM AUTHOR]

Abstract: The implementation of climate change mitigation strategies may significantly affect the current practices for electricity network operation. Increasing penetration of renewable energy generation technologies into electricity networks is one of the key mitigation strategies to achieve greenhouse gas emission reduction targets. Additional climate change mitigation strategies can also contribute to emission reduction thereby supplementing the renewable energy generation participation, which may be limited due to technical constraints of the network. In this paper, the penetration requirements for different renewable energy generation resources are assessed while concurrently examining other mitigation strategies to reduce overall emissions from electricity networks and meet requisite targets. The impacts of climate change mitigation strategies on the demand and generation mix are considered for facilitating the penetration of renewable generation. New climate change mitigation indices namely change in average demand, change in peak demand, generation flexibility and generation mix have been proposed to measure the level of emission reduction by incorporating different mitigation strategies. The marginal emissions associated with the individual generation technologies in the state of New South Wales (NSW) are modelled and the total emissions associated with the electricity grid of NSW are evaluated. [Copyright &y& Elsevier]

Abstract: Greenhouse gas (GHG) inventories underpinning the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol report each country's net annual emissions, that is GHG flows. Yet the UNFCCC's goal is defined as a stock (atmospheric GHG concentration). Flow inventories are apt for the fossil fuel sector where flows are effectively one way, stock changes are almost entirely anthropogenic, and stocks are stable in the absence of human perturbation. For the land sector, flow-based GHG inventories obscure fundamental differences between ecosystems: in their carbon stock stability, restoration capacity, and density. This paper presents a national carbon accounting framework that is comprehensive and includes stocks as well as flows for reservoirs, lands and activities continuously over time. It complements current flow-based inventories under the UNFCCC and Kyoto Protocol. The framework differentiates reservoirs by their role in the global carbon cycle, distinguishing between geocarbon (carbon in the geosphere), biocarbon (carbon in the biosphere) and anthropogenic carbon (stockpiles, products and waste). A reservoir ranking system is proposed based on longevity, reversibility of carbon loss, and carbon density. This framework will support policy makers and researchers grappling with mitigation strategies and competing demands on agricultural land and natural ecosystems. [Copyright &y& Elsevier]

Abstract: Integrated Assessment models, widely applied in climate change mitigation research, show that renewable energy sources (RES) play an important role in the decarbonization of the electricity sector. However, the representation of relevant technologies in those models is highly stylized, thereby omitting important information about the variability of electricity demand and renewables supply. We present a power system model combining long time scales of climate change mitigation and power system investments with short-term fluctuations of RES. Investigating the influence of increasingly high temporal resolution on the optimal technology mix yields two major findings: the amount of flexible natural gas technologies for electricity generation rises while the share of wind energy only depends on climate policy constraints. Furthermore, overall power system costs increase as temporal resolution is refined in the model, while mitigation costs remain unaffected. [Copyright &y& Elsevier]

A reliable energy supply is fundamental to ensure energy security and support the mitigation of climate change by promoting the use of renewable sources and reducing carbon emissions. Energy system analysis provides a sound methodology to assess energy needs, allowing to investigate the energy system behavior and to individuate the optimal energy-technology configurations for the achievement of strategic energy and environmental policy targets. In this framework, the estimation of future trends of exogenous variables such as energy demand has a fundamental importance to obtain reliable and effective solutions, contributing remarkably to the accuracy of models' input data. This study illustrates an application of regression analysis to predict energy demand trends in end use sectors. The proposed procedure is applied to characterize statistically the relationships between population and gross domestic product (independent variables) and energy demands of Residential, Transport and Commercial in order to determine the energy demand trends over a long-term horizon. The effectiveness of linear and nonlinear regression models for energy demand forecasting has been validated by classical statistical tests. Energy demand projections have been tested as input data of the bottom-up TIMES model in two applications (the TIMES-Basilicata and TIMES-Italy models) confirming the validity of the forecasting approach. • Energy systems modeling requires a detailed description of energy demand development (85). • Relationships among energy demand, population and economic variables are emphasized (83). • Regression analysis is applied to end-use demand forecasts as input for TIMES models (84). • The efficacy of regression analysis is validated by classical statistical tests (79). • Energy trends are tested with two energy models: TIMES-Basilicata and TIMES-Italy (81). [ABSTRACT FROM AUTHOR]