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Abstract This article focuses on the results obtained by the enforcement of a original greenhouse gas emissions policy, issued in recent years in several municipalities in Northern Italy. The Municipality of Reggio Emilia was one of the firsts in Italy to develop and adopt a voluntary building energy-consumption certification method called Ecoabita, aiming to promote energy efficiency in buildings over its jurisdiction. Several reasons make this project innovative: first, the certification method has been processed through and verified by a quality international agency, secondly the procedure is assisting financial-incentive policies both on mandatory (European Directive 2002/91/CE) and voluntary basis (Municipality Protocol Ecoabita), finally it allows to obtain eligible credits for avoided emissions that can be negotiated on the CO2 exchange market. With specific reference to Reggio Emilia case, acknowledged the amount of actions, that are normally scheduled in the municipality throughout a year, it has been evaluated the benefit, in terms of environmental improvement, descended from the protocol, also projecting their trend in future years up to 2020 as well, demonstrating the scheduled interventions are fundamental in sought of the achievement of the Kyoto and Copenhagen commitments. The entire procedure has been developed in accordance to the parameters given by UNFCCC for these kinds of analyses and has been recently confirmed to be one of the most accurate international accounting procedure by a survey developed by the scientific committee of the European Project LIFE-LAKS.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NCAR.CESM2-WACCM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CESM2-WACCM climate model, released in 2018, includes the following components: aerosol: MAM4 (same grid as atmos), atmos: WACCM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-06 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run by the National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA (NCAR) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 5 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED. 11 pages, 15 figures

Abstract Reaching universal access to electricity by 2030 requires a massive deployment of mini-grids in rural areas of developing countries. Among the many challenges hindering this process, there are the high uncertainties in assessing demand patterns in rural communities, the costs of field survey campaigns, and the absence of ample and reliable datasets coming from existing projects. This paper tries to address these issues by presenting and discussing a database of load profiles from sixty-one off-grid mini-grids from developing countries worldwide, gathered from the literature, private developers and fieldworks, and reported with technical, socio-economic and geographical characterization factors. A clustering procedure led to the identification of five archetypal load profile clusters, which are presented and analyzed together with their load duration curves. Subsequently, the distribution among the clusters of the various characterization factors selected is studied. The proposed approach allows to widen the range of load profiles usually considered, and to seek correlations between the load profile shapes, the peak power and average energy consumption per connection, the number of customers, the age of measurement, geographical position, operator model, type of tariff and generation technologies present. This work establishes a first step in the creation of a shared database for load profiles of rural mini-grids, helping to overcome the lack of available data and difficulties of demand assessment, proposing original insights for researchers to understand load patterns, and contributing to reduce risks and uncertainties for mini-grid developers.

Abstract This paper formulates a model that assesses the economic and policy implications of the adoption and operation of microgrids. The recent climate agreement between the US and China as well as the historic Lima accord on climate change are prompting a massive demand in climate-friendly technologies. Given the increasing demand for electricity and the mounting costs of electricity supply associated with maximizing system performance through an optimal economic scheduling of microgrids, decision makers and policymakers are already facing the problem of sketching energy management strategies for localized operation and grid penetration. We employ a modified Benders' decomposition approach to explore the optimal scheduling of supply from different technology units in the microgrid in the face of supply and demand uncertainties. We guarantee that the power generated by the units meets systemic load while satisfying the primary operational and technological constraints. We implement our model in a quantitative numerical analysis where we investigate the operational regulation in the light of a public environmental policy. We extend prior research on this subject by uncovering a greater reliance on the grid under simultaneous uncertainties in demand and supply. We unlock a strategy for a proper mitigation of the effects of emissions. We find that carbon abatement policy can be used to motivate microgrids operators to invest in fuel cells and natural gas technologies when dealing with uncertainty.

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants. 33 pages, 18 figures

To discuss current evidence of global climate change and its implications for allergic rhinitis and other allergic respiratory diseases.Global climate change is evidenced by increasing average earth temperature, increasing anthropogenic greenhouse gas levels, and elevated pollen levels. Pollutants of interest include carbon dioxide (CO2), ozone (O3), and nitrous oxide (NO2) because they can enhance the allergic response and lead to increased symptoms of allergic respiratory diseases. Heightened CO2 levels stimulate pollen production via photosynthesis and increased growth in multiple plant species investigated. Although worsened air quality appears to increase prevalence of allergic rhinitis, the effects of increased temperature are less certain. The findings of increased aeroallergen levels likely contribute to increases in presentation of allergic diseases, although more healthcare impact studies are necessary.Although recent literature indicates and strongly supports changes in temperature, pollution levels, and aeroallergen levels, more longitudinal epidemiologic surveillance of allergic diseases in relation to climate change as well as pathophysiologic studies on changing aeroallergen effects on allergic diseases are needed.

As part of the Copenhagen Accord, individual countries have submitted greenhouse gas reduction proposals for the year 2020. This paper analyses the implications for emission reductions, the carbon price, and abatement costs of these submissions. The submissions of the Annex I (industrialised) countries are estimated to lead to a total reduction target of 12-18% below 1990 levels. The submissions of the seven major emerging economies are estimated to lead to an 11-14% reduction below baseline emissions, depending on international (financial) support. Global abatement costs in 2020 are estimated at about USD 60-100 billion, assuming that at least two-thirds of Annex I emission reduction targets need to be achieved domestically. The largest share of these costs are incurred by Annex I countries, although the costs as share of GDP are similar for Annex I as a group and the seven emerging economies as a group, even when assuming substantial international transfers from Annex I countries to the emerging economies to finance their abatement costs. If the restriction of achieving two-thirds of the emission reduction target domestically is abandoned, it would more than double the international carbon price and at the same time reduce global abatement costs by almost 25%.