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The report gives a Literature Review of Zero Energy Buildings (ZEB) Definitions. It all startede when the consequences of the oil crisis became noticeable and the issue of the fossil fuels sources and the energy use were discussed. Nevertheless, in the late seventies and early eighties appeared few articles, in which phrases‘a zero energy house', ‘a neutral energy autonomous house' or ‘an energy-independent house' were used.' were used.

A wide range of measures has been proposed to improve vessel efficiency, reduce fuel consumption and lower emissions (1, 2). The classification of such measures is the subject of several publications. The Second GHG Study (3) of the International Maritime Organisation (IMO) is the most influential among them and identifies three fundamental categories of carbon emission reduction options: (i) energy efficiency improvements, which are further, sub-divided into the areas of ship design and operations; (ii) renewable energy sources; and (iii) fuels with lower lifecycle emissions per unit of work. Variations of this scheme have been proposed by Balland et al. (4) and Calleya, Pawling, and Greig (5). Classification schemes like the ones mentioned above are simple and practical but lack rigid theoretical foundations. On the other hand, schemes that attempt to capture the multiplicity of interrelations among all factors affecting emission volumes are often of low practical value due to their high level of complexity. IMO (3) provides such an example. Although it clearly acknowledges that, by definition, the CO2 emissions for most ships depend on the operational efficiency of the fleet and the transport work performed, when it comes to identifying the principal factors affecting the volume of emissions, the study presents a rather complex model including external and internal parameters that influence transport demand, modal split and fleetoperations among others. McKinnon’s analytical framework for green logistics falls into this category, too (6).

As one possibility to increase flexibility, battery storage systems (BSS) will play a key role in the decarbonization of the energy system. The emissions-intensity of grid electricity becomes more important as these BSSs are more widely employed. In this paper, we introduce a novel data basis for the determination of the energy system's CO2 emissions, which is a match between the ENTSO-E database and the EUTL databases. We further postulate four different dynamic emission factors (EF) to determine the hourly CO2 emissions caused through a change in electricity demand: the average emission factor (AEF), the marginal power mix (MPM), the marginal system response (MSR) and an energy-model-derived marginal power plant (MPP). For generic and battery storage systems, a linear optimization on two levels optimizes the economic and environmental storage dispatch for a set of 50 small and medium enterprises in Germany. The four different emission factors have different signaling effects. The AEF leads to the lowest CO2 reduction and allows for roughly two daily cycles. The other EFs show a higher volatility, which leads to a higher utilization of the storage system from 3.4 to 5.4 daily cycles. The minimum mean value for CO2 abatement costs over all 50 companies is 14.13 €/tCO2 .

Energy efficiency is widely considered a cost-effective strategy for reducing energy demand, energy-related carbon dioxide emissions and dependence on foreign energy sources. However, measuring the contribution of energy efficiency in reducing energy consumption and triggering economy-wide benefits is very complex, especially in large-scale process. In Finland, as well as other EU Member States, improvements in energy efficiency are measured against model-based scenarios and ex ante engineering estimates, which might provide an inaccurate indication of the actual energy savings delivered by energy efficiency. By employing decomposition analysis, this study isolates and quantifies the energy savings driven by energy efficiency improvements from the factors influencing the variation of final energy consumption in Finland over the 2005-2015 period. In addition, simple formulae are used to estimate the energy security and climate benefits associated with energy efficiency improvements. Overall, the results indicate modest energy savings as well as energy security and climate benefits due to energy efficiency improvements. Without energy efficiency improvements occurred between 2005 and 2015, the final energy consumption, the energy dependence and CO2 emissions in Finland would have hypothetically been 2.4%, 5%, and 3.5% higher, respectively, in 2015. If the energy and climate targets outlined in the Finland’s Integrated National Energy and Climate plan are to be met, a stronger political commitment to energy efficiency is needed. Future energy efficiency policies should be targeted at residential space heating demand, chemical, mining, food, and construction industries, and heavy-duty vehicles as energy efficiency was not effective in reducing energy consumption.

Following the Paris Agreement, most countries have agreed to reduce their CO2 emissions according to individually set Nationally Determined Contributions (NDCs). However, national CO2 emissions are reported by individual countries, and cannot be directly measured or ver- ified by third parties. This engenders a potential misreporting problem, where nations that are not living up to their Paris commitments could, by under-reporting emissions, nevertheless appear to be fulfilling their NDC targets. This paper uses the theory of sequential testing to design a statistical CO2 monitoring procedure, that can detect systematic misreportings of CO2 emissions. The data series that we monitor is the so-called carbon budget imbalance, which is a time series derived from reported CO2 emissions and independently measured Earth system data. We show that, when emissions are truthfully reported, the budget imbalance constitutes a stationary process, while, if emissions become systematically misreported, a structural break occurs. Our proposed procedure monitors the budget imbalance data and sequentially tests the null that the budget imbalance is stationary; rejection of the null provides evidence for system- atic misreportings of CO2 emissions. By constructing the procedure appropriately, detection time can be made sufficiently fast to help inform the 5 yearly global “stocktake” of the Paris Agreement.

We propose a structural augmented dynamic factor model for U.S. CO2 emissions. Variable selection techniques applied to a large set of annual macroeconomic time series indicate that CO2 emissions are best explained by industrial production indices covering manufacturing and residential utilities sectors. We employ a dynamic factor structure to explain, forecast, and nowcast the industrial production indices and thus, by way of the structural equation, emissions. We show that our model has good in-sample properties and out-of-sample performance in comparison with univariate and multivariate competitor models. Based on data through September 2019, our model nowcasts a reduction of about 2.6% in U.S. CO2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities.

In recent years there has been an increased focus on environmental issues within the building industry. For one thing this is due to the increased tightening of the demands to buildings energy consumption in order to reduce the CO2 emissions. As an example of this focus on low-energy buildings one may mention the Passive House concept. With this enlarged focus on low-energy buildings it has become even more important to integrate the large number of parameters and to be aware of the correlation between these. When trying to optimize buildings at several levels at the same time; concerning aesthetics (proportions, materials visual, physical and sensuous qualities etc.) as well as indoor climate, energy consumption, construction, user needs and economic etc. this will result in several different – and often conflicting – design parameters. This has caused an extensive need for methods to help structure the complexity of the design process and hereby ensure that all parameters are included in the final project