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Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.

The integration of residential photovoltaic (PV) systems for self-consumption is expected to unlock a variety of economic, technical and social benefits, such as the creation of local work and the reduction of the price of electricity, CO2 emissions and electrical power losses in the transmission network. In spite of all these advantages, regulatory barriers prevented the development of renewable self-consumption in Spain prior to October 2018, when a new regulation introduced the right to self-consume electrical energy without charges. This work aims to estimate the effect of residential self-consumption on the Iberian electricity market. To achieve this goal, a ceteris paribus approach was adopted, the accumulated scattered PV self-production was turned into a reduction of the aggregate demand in the market and the new clearing point was established. Based on 2017 market data, the results showed that a 1% curtailment of the annual demand due to PV self-consumption could reduce the cost of the market-traded energy by almost 2%. European Commission, Horizonte 2020 SI-1778/12/ 2018

Abstract Like cities, many large national parks in the United States often include “urban” visitor and residential areas that mostly demand (rather than produce) energy and key urban materials. The U.S. National Park Service has committed to quantifying and reducing scopes 1 and 2 emissions by 35% and scope 3 emissions by 10% by 2020 for all parks. Current inventories however do not provide the specificity or granularity to evaluate solutions that address fundamental inefficiencies in these inventories. By quantifying and comparing the importance of different inventory sectors as well as upstream and downstream emissions in Yosemite National Park (YNP), this carbon footprint provides a case study and potential template for quantifying future emissions reductions, and for evaluating tradeoffs between them. Results indicate that visitor-related emissions comprise the largest fraction of the Yosemite carbon footprint, and that increases in annual visitation (3.43–3.90 million) coincide with and likely drive interannual increases in the magnitude of Yosemite′s extended inventory (126,000–130,000 t CO2e). Given this, it is recommended that “per visitor” efficiency be used as a metric to track progress. In this respect, YNP has annually decreased kilograms of GHG emissions per visitor from 36.58 (2008) to 32.90 (2011). We discuss opportunities for reducing this measure further.

On 25 September 2015, the member states of the United Nations approved an initiative in New York called “Transforming our world: the 2030 Agenda for Sustainable Development” [...]

The assessment of greenhouse gases (GHGs) and air pollutants emitted to and removed from the atmosphere ranks high on international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need to consider the uncertainty in inventories of GHG emissions. The approaches to address uncertainty discussed in this special issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, system analytic perspective. They seek to strengthen the usefulness of national emission inventories under a compliance and/or global monitoring and reporting framework. The papers in this special issue demonstrate the benefits of including inventory uncertainty in policy analyses. The issues raised by the authors and featured in their papers, along with the role that uncertainty analysis plays in many of their arguments, highlight the challenges and the importance of dealing with uncertainty. While the Intergovernmental Panel on Climate Change (IPCC) clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of performing these studies go well beyond any suggestions made by the IPCC to date. Improving and conducting uncertainty analyses are needed to develop a clear understanding and informed policy. Uncertainty matters and is key to many issues related to inventorying and reducing emissions. Considering uncertainty helps to avoid situations that can create a false sense of certainty or lead to invalid views of subsystems. Dealing proactively with uncertainty allows for the generation of useful knowledge that the international community should have to hand while strengthening the 2015 Paris Agreement, which had been agreed at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). However, considering uncertainty does not come free. Proper treatment of uncertainty is demanding because it forces us to take the step from “simple to complex” and to grasp a holistic system view. Only, thereafter, can we consider potential simplifications. That is, comprehensive treatment of uncertainty does not necessarily offer quick or easy solutions for policymakers. This special issue brings together 13 papers that resulted from the 2015 (4th) International Workshop on Uncertainty in Atmospheric Emissions, in Cracow, Poland. While they deal with many different aspects of the uncertainty in emission estimates, they are guided by the same principal question: “What GHGs shall be verified at what spatio-temporal scale to support conducive legislation at local and national scales, while ensuring effective governance at the global scale?” This question is at the heart of mitigation and adaptation. It requires an understanding of the entire system of GHG sources and sinks, their spatial characteristics and the temporal scales at which they react and interact, the uncertainty (accuracy and/or precision) with which fluxes can be measured, and last but not least, the consequences that follow from all of the aforementioned aspects, for policy actors to frame compliance and/or global monitoring and reporting agreements. This bigger system context serves as a reference for the papers in the special issue, irrespective of their spatio-temporal focus, and is used as a guide for the reader.

AbstractSociety requires companies to adopt ethical and responsible behavior with regard to environmental policies in order to achieve sustainable development. One of the most controversial sectors in terms of sustainable production is the fashion industry. This study analyzes the extent to which seven of the biggest fashion brands in Spain and around the world communicate their Corporate Social Responsibility (CSR) policies (specifically, those related to sustainability). To this end, we identify the social media platforms used the most by the target audiences for these brands, and apply an exploratory, descriptive methodology to a content analysis of communication about their CSR policies, both formal (corporate websites) and informal (social media). The objective is to identify whether these companies communicate their sustainable activities and practices in the context of their CSR. Our findings confirm that the brands analyzed have integrated sustainability and environmental protection initiatives into their CSR policies. However, although they do communicate this on their corporate websites, they rarely do so on social media. We thus conclude that the seven fashion brands analyzed could make better use of social media to raise awareness and educate customers about environmental sustainability.

Temporary housing units (THUs), which are provided after disasters, are crucial in terms of sustainability pillars (economic, social, and environmental). In general, THUs, which are regular houses with minimum space and facilities, incorporate some negative aspects of the building industry. Additionally, as large numbers of THUs are usually provided in a short time and under emergency situations, some negative impacts of these units escalate. In this context, this study aims at reducing some negative impacts of THUs by applying a novel optimization model that maximises sustainability indexes by simulating the design of interior geometries for THUs. This method is based on the coupling of artificial intelligence and a multi-criteria decision-making model for sustainability assessment. The proposed model generates optimal solutions using a backtracking algorithm together with a binary search. To evaluate the sustainability indexes, an Integrated Value Model for Sustainability Assessment (MIVES) is applied. This novel method enables decision makers to automatically generate the most suitable alternative solutions for the early design stage of THUs. The results confirm that small changes in the interior geometric design can remarkably affect the sustainability indexes of THUs. The authors would like to acknowledge the kind support offered by professors and researchers from Shahid Beheshti University, Islamic Azad University, the Polytechnic University of Catalonia, and experts from the Housing Foundation of Islamic Republic of Iran (HFIR), who supported this paper for collecting and improving data. Additionally, the authors would like to thank UPC's Centre for Development Cooperation (CCD) for funding the authors (NO.: 2017-U006 and 2018-U012), which enabled the authors to follow and analyse the recovery program of Kermanshah earthquake. Peer Reviewed