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Abstract This study demonstrates the possibility to use energy-cost optimal building designs based on life cycle approach. There is a lack of studies that cover the comprehensive assessment of both embodied energy (EE) and operational energy (OE) in a single optimization problem. The primary goal of the current study is to compare the optimized results of using OE+EE together and OE only. The optimization is performed on a case study building (townhouse) in Finland with three structural alternatives (i.e., reinforced concrete (RC); cross-laminated timber (CLT) and Steel). Different options for insulation thickness of external wall, roof, floor and window types were considered as decision variables as the scope of the present study is on building envelope. The objectives of the optimization are to minimize life cycle energy (LCE) and life cycle costs (LCC). The LCE difference between the most and least energy efficient solution on the pareto front is greater in case of the OE optimization, compared to OE+EE optimization. For all studied structures, the EE of the optimal solutions from OE+EE optimization ranges 16%–23% of LCE. Many of the non-dominated optimal solutions obtained from the OE+EE optimization shows a higher U-value for the building envelope components compared to the optimal solutions from the OE optimization. A relationship between both OE and EE with the overall thermal resistance of the building envelope is discussed to obtain a deeper understanding of such differences in U-value for the optimal solutions obtained from the OE+EE and OE optimization.

Abstract This study demonstrates the possibility to use energy-cost optimal building designs based on life cycle approach. There is a lack of studies that cover the comprehensive assessment of both embodied energy (EE) and operational energy (OE) in a single optimization problem. The primary goal of the current study is to compare the optimized results of using OE+EE together and OE only. The optimization is performed on a case study building (townhouse) in Finland with three structural alternatives (i.e., reinforced concrete (RC); cross-laminated timber (CLT) and Steel). Different options for insulation thickness of external wall, roof, floor and window types were considered as decision variables as the scope of the present study is on building envelope. The objectives of the optimization are to minimize life cycle energy (LCE) and life cycle costs (LCC). The LCE difference between the most and least energy efficient solution on the pareto front is greater in case of the OE optimization, compared to OE+EE optimization. For all studied structures, the EE of the optimal solutions from OE+EE optimization ranges 16%–23% of LCE. Many of the non-dominated optimal solutions obtained from the OE+EE optimization shows a higher U-value for the building envelope components compared to the optimal solutions from the OE optimization. A relationship between both OE and EE with the overall thermal resistance of the building envelope is discussed to obtain a deeper understanding of such differences in U-value for the optimal solutions obtained from the OE+EE and OE optimization.

In an idealised industrial ecosystem (IE), firms and organisations utilise each other's material and energy flows including wastes and by-products to reduce the system's virgin material and energy input as well as the waste and emission output from the system as a whole, and contribute to sustainable development (SD). IE complements the more conventional individual flow, product, process, organisation, individual actor or sector-focused environmental management approaches and tools with network or systems level approaches. The first research objective of this paper is to construct indicators for IE. The second task is to test the use of these indicators with "what if?" material and energy flow scenarios for the energy and waste system of Satakunta region in Finland including 28 municipalities. Using literature analysis as a source, we arrive at environmental indicators of carbon dioxide (CO2) equivalents, and at economic indicators of fuel, energy and waste management costs and revenues. The social indicators show the employment effects of the waste management system. The scenarios analyse the current situation (0-scenario) against alternative situations in the future. The future scenarios are developed according to the known and anticipated trends in international and national policy and legislation. The indicator application in the scenarios produces social, environmental and economic effects of waste management in four categories: direct negative, direct positive, indirect negative and indirect positive. Industrial ecosystem theory emphasises the utilisation of wastes as a resource with value alongside the objective of reducing waste. Therefore, the indirect positive effects of waste management are important, as well as the conventional focus of waste management, which has usually been on direct positive effects. The main difficulties in our argument are the system boundary definition, the qualitatively different nature of environmental, economic and social effects and indicators as well as the lack of qualitative or interview data on the preferences and interests of the actors involved.

In an idealised industrial ecosystem (IE), firms and organisations utilise each other's material and energy flows including wastes and by-products to reduce the system's virgin material and energy input as well as the waste and emission output from the system as a whole, and contribute to sustainable development (SD). IE complements the more conventional individual flow, product, process, organisation, individual actor or sector-focused environmental management approaches and tools with network or systems level approaches. The first research objective of this paper is to construct indicators for IE. The second task is to test the use of these indicators with "what if?" material and energy flow scenarios for the energy and waste system of Satakunta region in Finland including 28 municipalities. Using literature analysis as a source, we arrive at environmental indicators of carbon dioxide (CO2) equivalents, and at economic indicators of fuel, energy and waste management costs and revenues. The social indicators show the employment effects of the waste management system. The scenarios analyse the current situation (0-scenario) against alternative situations in the future. The future scenarios are developed according to the known and anticipated trends in international and national policy and legislation. The indicator application in the scenarios produces social, environmental and economic effects of waste management in four categories: direct negative, direct positive, indirect negative and indirect positive. Industrial ecosystem theory emphasises the utilisation of wastes as a resource with value alongside the objective of reducing waste. Therefore, the indirect positive effects of waste management are important, as well as the conventional focus of waste management, which has usually been on direct positive effects. The main difficulties in our argument are the system boundary definition, the qualitatively different nature of environmental, economic and social effects and indicators as well as the lack of qualitative or interview data on the preferences and interests of the actors involved.

Biomethane is a flexible energy vector that can be used as a renewable fuel for both the heat and transport sectors. Recent EU legislation encourages the production and use of advanced, third generation biofuels with improved sustainability for future energy systems. The integration of technologies such as anaerobic digestion, gasification, and power to gas, along with advanced feedstocks such as algae will be at the forefront in meeting future sustainability criteria and achieving a green gas supply for the gas grid. This paper explores the relevant pathways in which an integrated biomethane industry could potentially materialise and identifies and discusses the latest biotechnological advances in the production of renewable gas. Three scenarios of cascading biomethane systems are developed.

Biomethane is a flexible energy vector that can be used as a renewable fuel for both the heat and transport sectors. Recent EU legislation encourages the production and use of advanced, third generation biofuels with improved sustainability for future energy systems. The integration of technologies such as anaerobic digestion, gasification, and power to gas, along with advanced feedstocks such as algae will be at the forefront in meeting future sustainability criteria and achieving a green gas supply for the gas grid. This paper explores the relevant pathways in which an integrated biomethane industry could potentially materialise and identifies and discusses the latest biotechnological advances in the production of renewable gas. Three scenarios of cascading biomethane systems are developed.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

Meteorological forcing at the air-water interface is the main determinant of the heat balance of most lakes (Edinger et al., 1968; Sweers, 1976). Year-to-year changes in the weather therefore have a major effect on the thermal characteristics of lakes. However, lakes that differ with respect to their morphometry respond differently to these changes (Gorham, 1964), with deeper lakes integrating the effects of meteorological forcing over longer periods of time. Other important factors that can influence the thermal characteristics of lakes include hydraulic residence time, optical properties and landscape setting (e.g. Salonen et al., 1984; Fee et al., 1996; Livingstone et al., 1999). These factors modify the thermal responses of the lake to meteorological forcing (cf. Magnuson et al., 2004; Blenckner, 2005) and regulate the patterns of spatial coherence (Chapter 17) observed in the different regions (Livingstone, 1993; George et al., 2000; Livingstone and Dokulil, 2001; Jarvinen et al., 2002; Blenckner et al., 2004)