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Growing scientific evidence for the indispensable role of environmental sustainability in sustainable development calls for appropriate frameworks and indicators for environmental sustainability assessment (ESA). In this paper, we operationalize and update the footprint-boundary ESA framework, with a particular focus on its methodological and application extensions to the national level. By using the latest datasets available, the planetary boundaries for carbon emissions, water use and land use are allocated to 28 selected countries in comparison to the corresponding environmental footprints. The environmental sustainability ratio (ESR)—an internationally comparable indicator representing the sustainability gap between contemporary anthropogenic interference and critical capacity thresholds—allows one to map the reserve or transgression of the nation-specific environmental boundaries. While the geographical distribution of the three ESRs varies across nations, in general, the worldwide unsustainability of carbon emissions is largely driven by economic development, while resource endowments play a more central role in explaining national performance on water and land use. The main value added of this paper is to provide concrete evidence of the usefulness of the proposed framework in allocating overall responsibility for environmental sustainability to sub-global scales and in informing policy makers about the need to prevent the planet’s environment from tipping into an undesirable state.

Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.

A membrane reactor containing an immobilized heterogeneous catalyst is an alternative for traditional homogeneous-based catalyzed transesterification for biodiesel production. Major problems in homogeneous catalysis are related to catalyst recuperation and soap formation, which can be overcome by using heterogeneous catalysts. Conversion can be increased by a combination of reaction and separation, using membranes with a specific pore size. The aim of this work was to study the performance of different membrane reactors combined with heterogeneous catalysis. The main objectives were: to identify a proper catalyst, to choose the proper immobilization technique, to establish the membrane with the adequate pore size, and to control the reaction and separation process. Amberlyst®15 with acid sites and different types of strontium oxide with basic sites were tested as heterogeneous catalysts. Strontium oxide provided the highest sunflower oil conversion (around 93%) and was easy to immobilize. Two catalytic membrane reactor configurations were investigated, thus confirming the production of several types of methyl esters. The configuration comprising the physical immobilization of the catalyst over the membrane reached a methyl ester yield of > 90 wt%.

AbstractNatural photosynthesis can be divided between the chlorophyll-containing plants, algae and cyanobacteria that make up the oxygenic phototrophs and a diversity of bacteriochlorophyll-containing bacteria that make up the anoxygenic phototrophs. Photosynthetic light harvesting and reaction centre proteins from both kingdoms have been exploited for solar energy conversion, solar fuel synthesis and sensing technologies, but the energy harvesting abilities of these devices are limited by each protein’s individual palette of pigments. In this work we demonstrate a range of genetically-encoded, self-assembling photosystems in which recombinant plant light harvesting complexes are covalently locked with reaction centres from a purple photosynthetic bacterium, producing macromolecular chimeras that display mechanisms of polychromatic solar energy harvesting and conversion. Our findings illustrate the power of a synthetic biology approach in which bottom-up construction of photosystems using naturally diverse but mechanistically complementary components can be achieved in a predictable fashion through the encoding of adaptable, plug-and-play covalent interfaces.

Flood risk is expected to increase in coastal cities, particularly in Asian megacities such as Shanghai. This paper presents an integrated modeling framework to simulate changes in the flood risk in Shanghai and provide a cost-benefit analysis of multiple adaptation strategies used to reduce risk. The results show that the potential flood risk will increase dramatically as a result of sea level rise, land subsidence, and socioeconomic development. By 2100, the expected annual damage could reach 0.8% (uncertainty range: 0.4%–1.4%) of local GDP under an optimistic emission scenario (RCP4.5), compared to the current value of 0.03%. All of the adaptation strategies can effectively reduce the flood risk under the current conditions and those in 2050. In contrast to the ‘hard’ flood protection strategies (i.e., storm-surge barriers and floodwalls), the ‘soft’ strategies (i.e., building codes and nature-based measures) cannot substantially reduce the flood risk in 2100. However, the soft strategies can play a critical role in reducing the residual risk resulting from the hard strategies. A ‘hybrid’ strategy combining a storm-surge barrier, wet-proofing, and coastal wetland development outperforms both hard and soft strategies in terms of low residual risk and high benefit/cost ratio. Additionally, the hybrid strategy can also enable a larger reduction in casualties. These findings imply that managing flood risk is more than the use of single adaptation measures. The methodology developed in this paper can enlighten Shanghai and other coastal cities on an economically and socially feasible adaptation strategy in an uncertain future.

This paper addresses the impact of endogenous technology through research and development (R&D) on the timing of climate change policy. We develop a model with a stock pollutant (carbon dioxide) and abatement technological change through R&D, and we use the model to study the interaction between carbon taxes and innovation externalities. Our analysis shows that the timing of optimal emission reduction policy strongly depends on the set of policy instruments available. When climate-specific R&D targeting instruments are available, policy has to use these to step up early innovation. When these instruments are not available, policy has to steer innovation through creating demand for emission saving technologies. That is, carbon taxes should be high compared to the Pigouvian levels when the abatement industry is developing. Finally, we calibrate the model in order to explore the magnitude of the theoretical findings within the context of climate change policy.

In this paper, we study how regulators may improve upon the efficiency of their energy technology adoption programs by exploiting readily observable information to limit rent extraction by firms. Using panel data on 862 investment decisions in the Netherlands, we find that rent extraction is closely linked not only to technology characteristics, but also to the firm's capital budgetting technique. In particular, we find that firms are more likely to extract rent when either the technology's pay-back period or its required investment is lower, but less likely if they do not use a formal capital budgeting technique. Standard firm characteristics, such as size and sector, correlate with firms' use of capital budgeting techniques, thereby partly resolving the regulator's asymmetric information problem.