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Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Renewable energy has become more popular since it is cost-effective and more efficient than conventional energy sources. Biomass-based renewable energy is primarily used in emerging economies to ensure environmental sustainability. This study examines the asymmetric correlation between biomass energy consumption and CO2 emissions in the top-10 biomass energy consumer countries (Brazil, Canada, Thailand, China, Italy, India, Germany, USA, UK, and Japan). A new approach "Quantile-onQuantile (QQ)" is employed by utilizing the data from 1991 to 2018. Biomass energy consumption, with the exception of Thailand, significantly mitigates CO2 emissions at various quantiles in selected countries. As a robustness check, we used the quantile regression test, whose findings are consistent with the outcomes from the quantile-on-quantile method. However, the degree of asymmetry in the biomass energy-CO2 nexus varies by country, necessitating extra attention and government vigilance when developing biomass energy and environmental policies.

Abstract In this study, intergeneric grafting was employed between Populus cathayana and Salix rehderiana to investigate the grafting compatibility of the two Salicaceae plants and to reveal whether grafting can improve their drought resistance. Under different grafting combinations (P. cathayana scion with P. cathayana rootstock, P/P; P. cathayana scion with S. rehderiana rootstock, P/S; S. rehderiana scion with S. rehderiana rootstock, S/S; and S. rehderiana scion with P. cathayana rootstock, S/P), the survival and growth rate, biomass accumulation and allocation, photosynthetic traits, carbon isotope composition (δ13C), relative water content (RWC) and non-structural carbohydrates (NSCs) were measured. The results showed that the grafting compatibility between P. cathayana and S. rehderiana was very high, as the survival rates ranged from 76% to 100% under different grafting combinations. Drought significantly decreased growth, biomass accumulation, photosynthetic pigment contents, net photosynthesis rates (Pn) and RWC, and increased δ13C in all grafting combinations. Under drought stress, biomass accumulation, total chlorophyll, transpiration rate (E) and Pn were higher in P/P and P/S than in S/S and S/P. Compared with P/P, the growth rate, biomass accumulation, root/aboveground ratio (R/A ratio), carotenoid, RWC, starch and total soluble sugar (TSS) of P/S were less affected by drought. The height growth rate (GRH), R/A ratio, carotenoid, chlorophyll a, total chlorophyll, WUEi and TSS of S/P were lower than those of S/S under water-limited conditions. Moreover, a principal component analysis indicated that P/S and S/S had higher drought resistance than P/P and S/P under water deficits. The used method allows combining specific advantageous traits from P. cathayana and S. rehderiana, which may be a highly useful tool to enhance drought resistance in the cultivation of Salicaceae plants.

This study investigates the relationship between violent conflict, food price, and climate variability at the subnational level. Using disaggregated data on 113 African markets from January 1997 to April 2010, interrelationships between the three variables are analyzed in simultaneous equation models. We find that: (i) a positive feedback exists between food price and violence - higher food prices increase conflict rates within markets and conflict increases food prices; (ii) anomalously dry conditions are associated with increased frequencies of conflict; and (iii) decreased rainfall exerts an indirect effect on conflict through its impact on food prices. These findings suggest that the negative effects of climate variability on conflict can be mitigated by interventions and effective price management in local markets. Creating environments in which food prices are stable and reliable, and markets are accessible and safe, can lower the impacts of both climate change and conflict feedbacks.

Sustainable development goals (SDGs) adopted in 2015 are geared toward sustainable development through various pathways, one being reducing inequality as covered in SDG 10. Inequalities are a threat to health and wellbeing of populations and a planet Earth in which we live. This rapid review aims to identify key issues that are likely to exacerbate inequalities around the six SDGs directly related to One Health, which are SDG 3, 6, 11, 13, 14 and 15, and suggest some actions that may help to address them using inclusive governance taking into account the coronavirus disease of 2019 (COVID-19) pandemic. Informed by the literature on SDGs and using the “inclusive development concept” by Gupta and Vegelin, literature search was done in Google Scholar, PubMed Central, as well as, searching of references in the relevant articles identified using search terms from the six SDGs that are directly related to One Health. In the context of the SDGs, in order to achieve One Health through inclusive governance, and tackle inequalities, the following needs to be considered and addressed: increasing number of armed conflicts; ongoing COVID-19 pandemic; ensuring availability of water and sanitation facilities; improving city and urban areas planning to cope with climate change; improving governance arrangements for addressing climate change factoring gender and human rights; multisectoral planning for conservation of oceans, seas, and marine resources; balancing trade regulation of wildlife trade with conservation efforts; need for a research collaborative involving experts from environmental sciences, wildlife, agriculture and human health to study and develop scientific evidence on contribution of changes in land use practices to occurrence of zoonotic diseases; and need of a legislation for promoting animal welfare to protect public health. Also, inclusion of people with disabilities in the use of digital technologies is critical.

This paper proposes a sustainability-oriented multiobjective optimization model for siting and sizing DG plants in distribution systems. Life cycle exergy (LCE) is used as a unified indicator of the entire system’s environmental sustainability, and it is optimized as an objective function in the model. Other two objective functions include economic cost and expected power loss. Chance constraints are used to control the operation risks caused by the uncertain power loads and renewable energies. A semilinearized simulation method is proposed and combined with the Latin hypercube sampling (LHS) method to improve the efficiency of probabilistic load flow (PLF) analysis which is repeatedly performed to verify the chance constraints. A numerical study based on the modified IEEE 33-node system is performed to verify the proposed method. Numerical results show that the proposed semilinearized simulation method reduces about 93.3% of the calculation time of PLF analysis and guarantees satisfying accuracy. The results also indicate that benefits for environmental sustainability of using DG plants can be effectively reflected by the proposed model which helps the planner to make rational decision towards sustainable development of the distribution system.

Sugar beet (Beta vulgaris ssp. vulgaris) is an important crop of temperate climates which provides nearly 30% of the world's annual sugar production and is a source for bioethanol and animal feed. The species belongs to the order of Caryophylalles, is diploid with 2n = 18 chromosomes, has an estimated genome size of 714-758 megabases and shares an ancient genome triplication with other eudicot plants. Leafy beets have been cultivated since Roman times, but sugar beet is one of the most recently domesticated crops. It arose in the late eighteenth century when lines accumulating sugar in the storage root were selected from crosses made with chard and fodder beet. Here we present a reference genome sequence for sugar beet as the first non-rosid, non-asterid eudicot genome, advancing comparative genomics and phylogenetic reconstructions. The genome sequence comprises 567 megabases, of which 85% could be assigned to chromosomes. The assembly covers a large proportion of the repetitive sequence content that was estimated to be 63%. We predicted 27,421 protein-coding genes supported by transcript data and annotated them on the basis of sequence homology. Phylogenetic analyses provided evidence for the separation of Caryophyllales before the split of asterids and rosids, and revealed lineage-specific gene family expansions and losses. We sequenced spinach (Spinacia oleracea), another Caryophyllales species, and validated features that separate this clade from rosids and asterids. Intraspecific genomic variation was analysed based on the genome sequences of sea beet (Beta vulgaris ssp. maritima; progenitor of all beet crops) and four additional sugar beet accessions. We identified seven million variant positions in the reference genome, and also large regions of low variability, indicating artificial selection. The sugar beet genome sequence enables the identification of genes affecting agronomically relevant traits, supports molecular breeding and maximizes the plant's potential in energy biotechnology.