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Carbon Side To Side Original Mix

Once an understanding of the gross anatomy of the human placenta is recognised, transfer studies must also consider the complex cellular organisation of the placental villus, the major site of exchange between maternal and fetal circulations (Fig. 2). The floating villi are covered in a multinucleated syncytium in direct contact with maternal blood. Underneath this lie the cytotrophoblast cells and the inner stromal core, containing placental macrophages, fibroblasts, and a capillary endothelium containing fetal blood5. Thus, for particles to move from maternal to fetal circulations, they must cross several cell layers and basement membranes. All these cell types arise from the blastocyst, and are therefore fetal in origin. Although fundamental to the interpretation of particle localisation, none of these cell types are mentioned in this study.

Carbon Side To Side Original Mix

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Carbon paper (originally carbonic paper) consists of sheets of paper that create one or more copies simultaneously with the creation of an original document when inscribed by a typewriter or ballpoint pen.

Carbon paper in its original form was paper coated on one side with a layer of a loosely bound dry ink or pigmented coating, bound with wax. The manufacture of carbon paper was formerly the largest consumer of montan wax. In 1954 the Columbia Ribbon & Carbon Manufacturing Company filed a patent for what became known in the trade as solvent carbon paper: the coating was changed from wax-based to polymer-based. The manufacturing process changed from a hot-melt method to a solvent-applied coating or set of coatings. It was then possible to use polyester or other plastic film as a substrate, instead of paper, although the name remained carbon paper.[3]

The purpose of this special issue is to broaden and strengthen the evidence base on the role of demand-side policies, measures, and corresponding mitigation pathways to limit global warming to 1.5 C. First, it addresses the knowledge gap regarding how demand-side mitigation options can contribute to achieving the 1.5 C goal. Second, it aims to raise the profile of demand-side options in the current policy and academic discourse focusing on the 1.5 C goal. Third, it seeks to bring together diverse research communities through a collection of deep decarbonization studies that provide a starting point for cross-disciplinary discussions of demand-side approaches.

Energy Efficiency has consistently addressed climate mitigation and demand-side issues since its inception. The journal provides critical analyses of demand-side issues in the transition to more sustainable energy systems. This special issue is consistent with this practice in bringing together a collection of articles that explore demand-side approaches and address the following research questions: Are deep decarbonization pathways in the transport sector compatible with the 1.5 C target? How can 1.5 C pathways be pursued through (radical) changes in household consumption? What is the role of potentially disruptive low-carbon innovation to limit warming to 1.5 C? What is the gap between national, bottom-up studies, and European (aggregated) 1.5 C scenarios? Which policy options will encourage deep decarbonization pathways in the building sector? What is the role of the sectoral policies for triggering an immediate peak in global emissions to keep the 1.5 C target within reach? To what extent can minimum performance standards complement carbon pricing? Will policies to promote energy efficiency help or hinder in achieving the 1.5 C climate target? What are the impacts of carbon pricing and energy-efficiency policies on electricity supply and demand in the USA? What policy interventions would foster near-zero carbon emissions in the residential heating sector? Taken as a whole, this special issue reflects the emerging knowledge on demand-side options and provides policy-relevant insights to complement the existing IAM literature.

Chen et al. (2018) consider stricter MEPS in various scenarios. The authors assume a 0.75% annual rate of efficiency improvements for a variety of technologies associated with heating, cooling, lighting, cooking, and hot water services. Combined with a carbon tax, they find that more ambitious performance standards are central to triggering deep decarbonization pathways in the Chinese building sector. The authors conclude that national performance standards should be significantly improved. Similarly, in the transport sector, Wachsmuth and Duscha (2018) and Gota et al. (2018) highlight that stringent fuel-efficiency standards should be put in place to lay the foundations for a meaningfully contribution to the 1.5 C goal.

Sonnenschein et al. (2018) argue that, in addition to performance standards and carbon pricing, further (mixes of) policy interventions that address behavioral anomalies should be studied (e.g., labelling programs, green defaults). Likewise, Gota et al. (2018) stress that current mitigation measures rely heavily on assumptions of behavioral change. Knobloch et al. (2018) find that the potential impacts of modeled policy instruments are highly dependent on assumptions of behavioral decision-making (cf. Kolstad et al. 2014; Mundaca et al. 2010; Worrell et al. 2004). The authors argue that a failure to acknowledge behavioral issues (e.g., bounded rationality) in modeling can lead to misguided policy recommendations and thus misleading outcomes. Consistent with the literature addressing demand-side issues in the domain of climate mitigation (Creutzig et al. 2016; Lucon et al. 2014; Sims et al. 2014), Gota et al. underline that not only do optimistic mitigation scenarios require greater behavioral change but also that mitigation potential due to behavioral options may be higher than is often assumed in modeling studies (see also Creutzig 2016). In line with the literature that addresses human behavior, climate mitigation, and modeling tools (e.g., Jochem et al. 2000; McCollum et al. 2017; Mundaca et al. 2010), the papers in the special issue implicitly (or explicitly) acknowledge the challenges associated with the parameterization of behavioral change in modeling tools.

Carbon pricing mechanisms (cap-and-trade schemes or carbon taxes) are often cited as a key part of the policy mix in 1.5 C (or 2 C) mitigation pathways (e.g., Bertram et al. 2015; Riahi et al. 2017; Rogelj et al. 2018; Rogelj et al. 2013; Stiglitz et al. 2017). In this particular case, stringent policy takes the form of carbon prices that are considerably higher than today. For example, in the building sector, Chen et al. (2018) model an economy-wide carbon tax ranging from 70 US$/tCO2 in 2020, 300 US$/tCO2 in 2050 to 1265 US$/tCO2 in 2100. Combined with other policy assumptions related to ambitious minimum performance standards and building codes, the tax regime accelerates the implementation of mitigation measures, both on the supply side and in the building sector. The authors conclude that a combination of carbon pricing and technology-oriented policies is needed if the Chinese building sector is to meet the 1.5 C goal.

Modeling deep decarbonization pathways in the residential heating sector, Knobloch et al. (2018) use a sectoral carbon tax to drive mitigation measures. In this case, a carbon tax increases the price of fossil fuels relative to their carbon content. Different scenarios are modeled using a tax in the range of 50 US$/tCO2 to 100 US$/tCO2 in 2020 and from 200 US$/tCO2 to 400 US$/tCO2 in 2050. Carbon taxes are either implemented in isolation or combined with technology subsidies, a procurement scheme and building codes. Their results show that it is this combination of policies that is most effective in driving market uptake of fuel-efficient low-carbon technologies.

Brown and Li (2018) analyze mitigation scenarios based on a carbon tax in the US electricity sector, either implemented in isolation or combined with other policies (e.g., MEPS). Carbon prices are relatively lower than those modeled by Chen et al. and Méjean et al. and range from 10 US$/tCO2, 20 US$/tCO2, and 40 US$/tCO2 in 2020 to 26 US$/tCO2, 53 US$/tCO2, and 59 US$/tCO2 in 2040. Results show that a mix of energy-efficiency policies and a carbon tax that increases from 10 US$/tCO2 in 2020 to 27 US$/tCO2 in 2040 delivers net savings. Interestingly, a higher tax (ranging from 20 US$/tCO2 in 2020 to 53 US$/tCO2 in 2040) is shown to deliver the same emission reductions and keep the sector within the 1.5 C target; however costs are much higher in the absence of complementary demand-side policies. This emphasizes that increased energy efficiency (or lower energy intensity) reduces mitigation costs (Grubler et al. 2018; Luderer et al. 2013; Riahi et al. 2015; Rogelj et al. 2013).

Finally, ambitious policies often take the form of increasing the stringency of existing measures while simultaneously implementing new, bold interventions. This process needs to be coordinated, and policy evaluation is crucial. In addition to the implementation of carbon pricing alongside demand-side policies, various papers underline the need for comprehensive and integrated policy mixes.

The articles that analyze demand-side measures in the buildings sector stress the critical need for rapid improvements in energy efficiency if the 1.5 C target is to remain viable. Consistent with previous IPCC Assessment Reports (Levine et al. 2007; Lucon et al. 2014), their findings confirm the value of several mitigation measures and opportunities.

Méjean et al. (2018) analyze global emission peaks intended to meet the 1.5 C goal and the resulting dynamics across various sectors, including the residential sector. First, they show that a post-2030 peak makes the 1.5 C goal unachievable but, if it is reached earlier (in 2020), direct emissions in the residential sector peak at nearly the same time. This finding indicates the need for a high degree of policy coordination between international climate policy and specific sectoral interventions. From a global perspective, the study finds that the residential sector contributes relatively less to an earlier peak than others. However, these results are sensitive to assumptions about energy-demand patterns and corresponding policies, which critically affect the timing of peaks. 041b061a72

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