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The EU has long pursued relatively ambitious climate and energy policies, often against the backdrop of what has been termed the EU ‘polycrisis’. This paper introduces a special issue which seeks t...

As a consequence of energy issues and societal dilemmas, building insulation like the eco-block technology has proven its usefulness to reduce greenhouse gas emissions, improve energy security and enhance the living conditions of building occupants. In Mauritius, the eco-block building material was introduced to improve the energy efficiency of conventional buildings. As observed, limited people's knowledge, poor communication with developers and lack of support from policymakers slow the adoption of the building technology. Mauritian residents are the important stakeholders as they are the final decision-makers of the building insulation. Given recent studies reported that green building adoption is rather psychological than technical, the research makes an original contribution to the literature by extending two consistent psychological frameworks (technology of acceptance model and theory of planned behaviour) and proposing a new framework for assessing the unexplored predictors on eco-block building adoption. A survey questionnaire was forwarded to Mauritian residents through the purposive sampling method to collect data, where 283 responses were useful to undergo structural equation modelling. The results disclosed that attitudes, perceived usefulness, social norm, perceived behavioural control, personal innovativeness, energy concern and price sensitivity have an impact on the acceptance of the eco-block building. Contrarily, subjective knowledge and organisational trust have no influence on the adoption intention. Instead, organisational trust affects behavioural intention indirectly through perceived usefulness. The outcome of this research can serve as a roadmap for relevant stakeholders to promote eco-block building usage in Mauritius.

The aim of this paper is to present a decision support system (DSS) to capture the complexity of the transition of a national energy system to net zero in the context of multiple sustainability themes. The paper proposes an integrated assessment framework that combines dynamic systems modeling, sustainability indicators, and multi-criteria decision analysis (MCDA) with direct stakeholder involvement. To illustrate the use of the DSS, the paper compares bundles of climate change policies that aim to decarbonize the road transport sector in Iceland. Eighteen scenarios and alternative development trajectories are defined for the Icelandic energy system based on a combination of three main driving forces. These are, firstly, economic development (three cases); secondly, changes in energy efficiency (two cases); and finally, three climate policy bundles aimed at increasing the share of electric vehicles. Based on the results from the integrated assessment framework, the performance scores of the climate policy bundles are compared across the following five sustainability themes: social impact; economic development; environmental impact; energy security; and technical aspects. The findings confirm that a different conclusion may be reached when multiple sustainability themes are applied in the selection of preferred policy bundles as compared to conventional techno-economic criteria. Banning the registration of fossil-fueled vehicles, combined with economic instruments, offers the best decarbonizing strategy to reach climate and energy policy goals simultaneously.

Climate adaptation is a dynamic social and institutional process where the governance dimension is receiving growing attention. Adaptive governance is an approach that promises to reduce uncertainty by improving the knowledge base for decision making. As uncertainty is an inherent feature of climate adaptation, adaptive governance seems to be a promising approach for improving climate adaptation governance. However, the adaptive governance literature has so far paid little attention to decisionmaking tools and methods, and the literature on the governance of adaptation is in its infancy in this regard. We argue that climate adaptation governance would benefit from systematic and yet flexible decision-making tools and methods such as participatory multicriteria methods for the evaluation of adaptation options, and that these methods can be linked to key adaptive governance principles. Moving from these premises, we propose a framework that integrates key adaptive governance features into participatory multicriteria methods for the governance of climate adaptation. © 2014 by the author(s).

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Along with total radiation received, the proportion of diffuse to direct solar radiation can influence forest photosynthesis and carbon cycling. However, tropical diffuse radiation regimes are poorly described, and to date there are few or no site-based or regional diffuse radiation datasets. The relationship between cloud fraction and diffuse solar radiation was investigated using data from two sites in western and eastern Amazonia. Radiation regimes for diffuse and total radiation were characterised for each site, and the variation in clear sky diffuse radiation fraction between wet and dry season demonstrated and quantified, as well as the dependence of diffuse radiation on cloud amount. Using high frequency measurements of diffuse and total solar radiation data from the two sites, and estimated top of the canopy clear-sky radiation, a number of alternative models to predict diffuse radiation fraction from cloud fraction were formulated and tested. Results showed that cloud fraction can be approximated using the relationship between observed and calculated top of canopy radiation, after which diffuse radiation can then be predicted from cloud fraction. We also demonstrate that satellite cloud data (from the International Satellite Cloud Climatology Project) can be used as inputs to the diffuse radiation model to provide estimates of annual and monthly diffuse radiation proportion.

Global emissions of carbon dioxide (CO 2 ) from fossil fuels and industry increased by 2.2% per year on average between 2005 and 2015 1 . Global emissions need to peak and decline rapidly to limit climate change to well below 2 °C of warming 2,3 , which is one of the goals of the Paris Agreement 4 . Untangling the reasons underlying recent changes in emissions trajectories is critical to guide efforts to attain those goals. Here we analyse the drivers of decreasing CO 2 emissions in a group of 18 developed economies that have decarbonized over the period 2005–2015. We show that within this group, the displacement of fossil fuels by renewable energy and decreases in energy use explain decreasing CO 2 emissions. However, the decrease in energy use can be explained at least in part by a lower growth in gross domestic product. Correlation analysis suggests that policies on renewable energy are supporting emissions reductions and displacing fossil fuels in these 18 countries, but not elsewhere, and that policies on energy efficiency are supporting lower energy use in these 18 countries, as well as more widely. Overall, the evidence shows that efforts to reduce emissions are underway in many countries, but these efforts need to be maintained and enhanced by more stringent policy actions to support a global peak in emissions followed by global emissions reductions in line with the goals of the Paris Agreement 3 .