Humanity faces several environmental challenges today. The planet has limited resources, and it is necessary to use these resources effectively. This paper examines the environmental impact of three energy solutions for the heating and cooling of buildings. The solutions are conventional district heating and cooling, a smart energy solution for heating and cooling (ectogrid™), and geothermal energy. The ectogrid™ balances energy flows with higher and lower temperatures to reduce the need for supplied energy. The three solutions have been studied for Medicon Village, which is a district in the city of Lund in Sweden. The study shows that the energy use for the conventional system is 12,250 MWh for one year, and emissions are 590 tons of CO₂ equivalents. With ectogrid™, the energy use is reduced by 61%, and the emissions are reduced by 12%, compared to the conventional system. With geothermal energy, the energy use is reduced by 70%, and the emissions by 20%. An analysis is also made in a European context, with heating based on natural gas and cooling based on air conditioners. The study shows that the environmental impact would decrease considerably by replacing the carbon dioxide intensive solution with ectogrid™ or geothermal energy.

Energy efficiency (EE) improvement is one of the most crucial elements in the decarbonization of industry. EE potential within industries largely remains untapped due to the lack of information regarding potential EE measures (EEM), knowledge regarding energy use, and due to the existence of some inconsistencies in the evaluation of energy use. Classification of energy end-using processes would increase the understanding of energy use, which in turn would increase the detection and deployment of EEMs. The study presents a novel taxonomy with hierarchical levels for energy end-use in manufacturing operations for the engineering industry, analyzes processes in terms of energy end-use (EEU) and CO₂ emissions, and scrutinizes energy performance indicators (EnPIs), as well as proposing potential new EnPIs that are suitable for the engineering industry. Even though the study has been conducted with a focus on the Swedish engineering industry, the study may be generalizable to the engineering industry beyond Sweden.

Sweden implemented a unique policy instrument over two consecutive five-year periods aimed at promoting industrial energy efficiency: a voluntary agreement programme (VAP) known as the Programme for Improving Energy Efficiency in Energy-Intensive Industries (PFE). This paper evaluates the second programme period using process and impact evaluation approaches. The impact evaluation revealed that the programme generated numerous benefits beyond the implementation of energy efficiency measures. The second programme period demonstrated impacts comparable to the first, underscoring the pivotal role of energy management within policy frameworks. Moreover, the findings suggest that VAPs may be among the few policy instruments capable of achieving substantial impact within industrial production processes. The process evaluation identified two critical gaps: a policy gap and a knowledge gap. Many of the positive elements present in the evaluated programme are currently absent from Sweden’s industrial policy mix. The role of the policy operator—as facilitator or intermediary—proved essential, particularly in the context of policies involving energy audits and certified energy management systems. In this case, the Swedish Energy Agency played a central role. The study concludes that continuous energy management, when integrated throughout the entire organization, significantly enhances companies’ capacity to identify and implement energy efficiency measures. This approach not only deepens internal knowledge but also fosters the development of more structured and effective routines. Furthermore, the findings suggest that financial incentives may be essential to ensure the long-term persistence of energy efficiency practices. Consequently, future policy design should include a strong facilitator and incorporate mechanisms that both reinforce organizational knowledge of energy end-use processes and promote sustained engagement over time, preferably involving a financial incentive.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biased and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biases in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles, and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness, whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance.