The Paris Climate Accord calls for urgent CO2 reductions. Here we investigate low and zero carbon pathways based on clean electricity and sector coupling. Effects from different spatialities are considered through city and national cases (Helsinki and Finland). The methodology employs techno-economic energy system optimization, including resilience aspects. In the Finnish case, wind, nuclear, and biomass coupled to power-to-heat and other flexibility measures could provide a cost-effective carbon-neutral pathway (annual costs −18%), but nuclear and wind are, to some extent, exclusionary. A (near) carbon-neutral energy system seems possible even without nuclear (−94% CO2). Zero-carbon energy production benefits from a stronger link to the broader electricity market albeit flexibility measures. On the city level, wind would not easily replace local combined heat and power (CHP), but may increase electricity export. In the Helsinki case, a business-as-usual approach could halve emissions and annual costs, while in a comprehensive zero-emission approach, the operating costs (OPEX) could decrease by 87%. Generally, electrification of heat production could be effective to reduce CO2. Low or zero carbon solutions have a positive impact on resilience, but in the heating sector this is more problematic, e.g., power outage and adequacy of supply during peak demand will require more attention when planning future carbon-free energy systems.

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One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents datadriven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the LevenbergMarquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

The Paris Climate Accord and recent IPCC analysis urges to strive towards carbon neutrality by the middle of this century. As most of the end-use energy in Europe is for heating, or well above 60%, these targets will stress more actions in the heating sector. So far, much of the focus in the emission reduction has been on the electricity sector. For instance, the European Union has set as goal to have a carbon-free power system by 2050. Therefore, the efficient coupling of renewable energy integration to heat and heating will be part of an optimal clean energy transition. This paper applies optimization-based energy system models on national (Finland) and sub-national level (Helsinki) to include the heating sector in an energy transition. The models are based on transient simulation of the energy system, coupling variable renewable energies (VRE) through curtailment and power-to-heat schemes to the heat production system. We used large-scale wind power schemes as VRE in both cases. The results indicate that due to different energy system limitations and boundary conditions, stronger curtailment strategies accompanied with large heat pump schemes would be necessary to bring a major impact in the heating sector through wind power. On a national level, wind-derived heat could meet up to 40% of the annual heat demand. On a city level, the use of fossil fuel in combined heat and power production (CHP), typical for northern climates, could significantly be reduced leading even close to 70% CO 2 emission reductions in Helsinki. Though these results were site specific, they indicate major opportunities for VRE in sectoral coupling to heat production and hence also a potential role in reducing the emissions.