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This dataset contains the data used in the manuscript "Physiological adaptation of the diatom Pseudo-nitzschia delicatissima under copper starvation" accepted for publication in April 2023 in Marine Environmental Research. In the open ocean and particularly in iron (Fe)-limited environment, copper (Cu) deficiency might limit the growth of phytoplankton species. Cu is an essential trace metal used in electron-transfer reactions, such as respiration and photosynthesis, when bound to specific enzymes. Some phytoplankton species, such as the diatom Pseudo-nitzschia spp. can cope with Cu starvation through adaptative strategies. This dataset contains the data collected during the experimental starvation of a strain of the diatom P. delicatissima under laboratory controlled conditions.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

Abstract This work aims to predict the general performances of a pilot parabolic trough collector during transient periods. To do so, a one-dimensional thermal model has been developed. It has been validated with experimental results from two different experimental setups, in steady-state conditions, with a transmitted power maximum error of 3.4%. Since the model only predicts the collector's thermal behavior, the parabolic trough collector has been first optically qualified. Then, optical efficiencies were used as input for the model. Experimental results were obtained in steady-state conditions and compared to the model. Then, experimental and numerical results were compared during two period of time with varying inlet conditions (i.e. dynamic condition tests): the first one with stable conditions, and the other one with harsh conditions. The developed model showed a good capability of predicting the thermal behavior of the parabolic trough collector with unstable environment (DNI, mass flow, inlet temperature), with a 9.6% relative standard error in the worst case. As a conclusion, while previous studies only focused on steady-state conditions, it has been showed that this kind of model can be used to precisely predict the dynamic behavior of large power plants.

Abstract This paper investigates the optimal choice of building energy efficiency (BEE) standard in the context of centralised urban district heating system in northern China. By employing a techno-economic analysis approach, we demonstrate that the current BEE standard implemented in the Chinese cities should be tightened further in order to achieve a socially optimal level. Without considering the externality costs associated with carbon dioxide (CO2) emissions, current BEE standards need to be upgraded to the equivalent level of French RT2005 standard coupled with a properly designed district coal-fired Combined Heat and Power (CHP). In contrast, the equivalent efficiency standard of Swedish building code is preferably to be implemented in the case of explicit carbon emission restriction as long as the marginal cost of carbon emission (carbon price) is sufficiently high. The fuel-switching policy (from coal to natural gas) in the urban district heating system would result in significant increase in overall costs if the BEE upgrade is not taken into account simultaneously. It is also found that BEE improvements in northern Chinese cities are more cost-effective than investing in low-carbon technologies such as wind power or Carbon Capture and storage in the EU and US with regard to CO2 emissions mitigation.

Abstract Building-based active envelopes play an important role to reduce active heating supplies. Several techniques are developed to enhance the energy performances of active building envelopes; meanwhile, numerous numerical and mathematical models are also developed to conduct the performance analysis of these techniques. In this paper, we propose a state-space model for solar active wall-based Phase Change Materials (PCM). The advantage of this method remains in its simplicity to provide details of internal nodes and input/output parameters. The low-cost calculation is a supplementary advantage versus a heavy numerical method. The proposed numerical model is applied for a multi-layer wall with PCM Wallboards (PCMW) embedded between indoor and outdoor environments. The results show the ability of the state-space model to estimate the thermal behavior of the system, as well as the thermal characteristics of embedding PCM in the internal face of the wall. It significantly contributes to stabilize the indoor temperature and to ensure the thermal comfort.

Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour.

This chapter focuses on the evaluation of adaptive capacities of community-level human systems related to agriculture and food security. It highlights findings regarding approaches and domains to monitor and evaluate behavioral changes from CGIAR’s research program on climate change, agriculture and food security (CCAFS). This program, implemented in five West African countries, is intended to enhance adaptive capacities in agriculture management of natural resources and food systems. In support of participatory action research on climate-smart agriculture, a monitoring and evaluation plan was designed with the participation of all stakeholders to track changes in behavior of the participating community members. Individuals’ and groups’ stories of changes were collected using most significant change tools. The collected stories of changes were substantiated through field visits and triangulation techniques. Frequencies of the occurrence of characteristics of behavioral changes in the stories were estimated. The results show that smallholder farmers in the intervention areas adopted various characteristics of behavior change grouped into five domains: knowledge, practices, access to assets, partnership and organization. These characteristics can help efforts to construct quantitative indicators of climate change adaptation at local level. Further, the results suggest that application of behavioral change theories can facilitate the development of climate change adaptation indicators that are complementary to indicators of development outcomes. We conclude that collecting stories on behavioral changes can contribute to biophysical adaptation monitoring and evaluation.