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Energy system optimization models (ESOMs) such as MARKAL/TIMES are used to support energy policy analysis worldwide. ESOMs cover the full life-cycle of fuels from extraction to end-use, including the associated direct emissions. Nevertheless, the life-cycle emissions of energy equipment and infrastructure are not modelled explicitly. This prevents analysis of questions relating to the relative importance of emissions associated with the build-up of infrastructure and other equipment required for decarbonization.
The experience curve theory assumes that technology costs decline as experience of a technology is gained through production and use. This article reviews the literature on the experience curve theory and its empirical evidence in the field of electricity generation technologies. Differences in the characteristics of experience curves found in the literature are systematically presented and the limitations of the experience curve theory, as well as its use in energy models, are discussed. The article finds that for some electricity generation technologies, especially small-scale modular technologies, there has been a remarkably strong (negative) relationship between experience and cost for several decades. Conversely, for other technologies, especially large-scale and highly complex technologies, the experience curve does not appear to be a useful tool for explaining cost changes over time. The literature review suggests that when analysing past cost developments and projecting future cost developments, researchers should be aware that factors other than experience may have significant influence. It may be worthwhile trying to incorporate some of these additional factors into energy system models, although considerable uncertainties remain in quantifying the relevance of some of these factors.
Tackling fuel poverty has become an increasingly important issue on many European countries' political agendas. Consequently, national governments, local authorities and NGOs have established policies and programmes to reduce the fuel poverty vulnerability of households. However, evaluations of such policies and programmes show that they barely reach those who are most in need. The reasons for this failure are diverse and include fuel poverty measurement metrics, local scale data availability and policy design. This raises the question of how fuel poor homes can be more effectively identified and targeted to ensure that limited local and national budgets are used to benefit those who most need help.
Area-based approaches, which pinpoint spatial units highly affected by fuel poverty due to their specific characteristics, offer an opportunity for creating more tailored policies and programmes. In this study, the author developed a GIS-MCDA (Multi-Criteria Decision Analysis), using an AHP (Analytical Hierarchy Process) and applied the approach to the German city of Oberhausen. The overall issue of fuel poverty was broken down into three vulnerability dimensions (heating burden, socio-economic and building vulnerability), the relative importance of fuel poverty criteria and the dimensions were evaluated by experts, and an overall Fuel Poverty Index was created to assess the relative fuel poverty vulnerability of 168 urban neighbourhoods.
The analysis offers insights into the spatial pattern of fuel poverty within a city and thus provides an opportunity to channel efforts towards households in those neighbourhoods most in need. It also demonstrates that a trade-off between ecological and social targets should be considered in the development of future policies for tackling fuel poverty.
Considering the role of transport for a 1.5 Degree stabilization pathway and the importance of light-duty vehicle fuel efficiency within that, it is important to understand the key elements of a policy package to shape the energy efficiency of the vehicle fleet. This paper presents an analysis focusing on three types of policy measures: (1) CO2 emission standards for new vehicles, (2) vehicle taxation directly and indirectly based on CO2 emission levels, and (3) fuel taxation. The paper compares the policies in the G20 economies and estimates the financial impact of those policies using the example of a Ford Focus vehicle model. This analysis is a contribution to the assessment of the role of the transport sector in global decarbonisation efforts. The findings of this paper show that only an integrated approach of regulatory and fiscal policy measures can yield substantial efficiency gains in the vehicle fleet and can curb vehicle kilometres travelled by individual motorised transport. Using the illustrative example of one vehicle model, the case study analysis shows that isolated measures, e.g. fuel efficiency regulation without corresponding fuel and vehicle taxes only have minor CO2 emission reduction effects and that policy measures need to be combined in order to achieve substantial emission reduction gains over time. The analysis shows that the highest level of impact is achieved by a combination regulatory and fiscal policies rather than only one policy even if this policy is more aggressive. When estimating the quantitative effect of fuel efficiency standards, vehicle and fuel tax, the analysis shows that substantial gains with regard to CO2 emission are only achieved at a financial impact level above 500 Euros over a four year period.
Previous studies showed that using carbon dioxide (CO2) as a raw material for chemical syntheses may provide an opportunity for achieving greenhouse gas (GHG) savings and a low-carbon economy. Nevertheless, it is not clear whether carbon capture and utilization benefits the environment in terms of resource efficiency. We analyzed the production of methane, methanol, and synthesis gas as basic chemicals and derived polyoxymethylene, polyethylene, and polypropylene as polymers by calculating the output-oriented indicator global warming impact (GWI) and the resource-based indicators raw material input (RMI) and total material requirement (TMR) on a cradle-to-gate basis. As carbon source, we analyzed the capturing of CO2 from air, raw biogas, cement plants, lignite-fired power, and municipal waste incineration plants. Wind power serves as an energy source for hydrogen production. Our data were derived from both industrial processes and process simulations. The results demonstrate that the analyzed CO2-based process chains reduce the amount of GHG emissions in comparison to the conventional ones. At the same time, the CO2-based process chains require an increased amount of (abiotic) resources. This trade-off between decreased GHG emissions and increased resource use is assessed. The decision about whether or not to recycle CO2 into hydrocarbons depends largely on the source and amount of energy used to produce hydrogen.
The Paris Agreement (PA) emphasizes the intrinsic relationship between climate change and sustainable development (SD) and welcomes the 2030 agenda for the global Sustainable Development Goals (SDGs). Yet, there is a lack of assessment approaches to ensure that climate and development goals are achieved in an integrated fashion and trade-offs avoided. Article 6.4 of the PA introduces a new Sustainable Mitigation Mechanism (SMM) with the dual aim to contribute to the mitigation of greenhouse gas emissions and foster SD. The Kyoto Protocol's Clean Development Mechanism (CDM) has a similar objective and in 2014, the CDM SD tool was launched by the Executive Board of the CDM to highlight the SD benefits of CDM activities. This article analyses the usefulness of the CDM SD tool for stakeholders and compares the SD tool's SD reporting requirements against other flexible mechanisms and multilateral standards to provide recommendations for improvement. A key conclusion is that the Paris Agreement's SMM has a stronger political mandate than the CDM to measure that SD impacts are "real, measurable and long-term". Recommendations for an improved CDM SD tool are a relevant starting point to develop rules, modalities, and procedures for SD assessment in Article 6.4 as well as for other cooperative mitigation approaches.