Arcs of carbon awareness in the value chain and their antecedents

Nowadays, the upstream providers of Yunnan germanium value chain share small value, failed to get the reasonable value share; therefore the research on the germanium value chain of Yunnan has important significance. Firstly, this paper analyze the concept of germanium value chain based on the existing research on value chain, then combine with the actual situation of the industry of germanium, from the basic activities and supporting activities construction of value chain of germanium; and then analyze the value chain deficiencies of Yunnan germanium industry from the upstream value chain, midstream value chain, downstream value chain and value chain integration. Aiming at these deficiencies, proposing the targeted measures and suggestions to improve customer satisfaction, occupy the key position of germanium industrial value chain, and improve coordination of upstream and downstream enterprises.

Increasing food production to meet rising global demand while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions is one of the greatest challenges facing the agri-food sector. Sustainable food production relates not only to primary production, but also has wider value chain implications. An input-output (IO) model is a modelling framework which contains information on the flow of goods and services across a value chain at a regional or national economy level. This paper provides a detailed description of the development of a Bioeconomy IO (BIO) model which is disaggregated across the subs-sectors of the agri-food value chain and environmentally extended (EE) to examine environmental outputs, including GHG emissions, We focus on Ireland, where emissions from agriculture comprise 33% of national GHG emissions and where there has been a major expansion and transformation in agriculture supported by national and EU policy. In a substantial Annex to this paper, we describe the modelling assumptions made in developing the BIO model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed are higher for poultry than for other value chains, given the lower animal based emissions from poultry than from cows or sheep. The level of disaggregation also shows that the sub-sectors are themselves discrete value chains. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry. The analysis facilitated by the BIO model also allows for the mapping of emissions along the agri-food value chain using the adapted IO EE approach. Such analysis is valuable in identifying emissions ‘hot-spots’ along the value chains and analysing potential avenues for emission efficiencies.

Imposing versus Enacting Commitments for the Long‐Term Energy Transition: Perspectives from the Firm

Increasing food production to meet rising global demand while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions is one of the greatest challenges facing the agri‐food sector. Sustainable food production relates not only to primary production, but also has wider value chain implications. An input‐output (IO) model is a modelling framework which contains information on the flow of goods and services across a value chain at a regional or national economy level. This paper provides a detailed description of the development of a Bioeconomy IO (BIO) model which is disaggregated across the subs‐sectors of the agri‐food value chain and environmentally extended (EE) to examine environmental outputs, including GHG emissions, We focus on Ireland, where emissions from agriculture comprise 33% of national GHG emissions and where there has been a major expansion and transformation in agriculture supported by national and EU policy. In a substantial Annex to this paper, we describe the modelling assumptions made in developing the BIO model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed are higher for poultry than for other value chains, given the lower animal based emissions from poultry than from cows or sheep. The level of disaggregation also shows that the sub‐sectors are themselves discrete value chains. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry. The analysis facilitated by the BIO model also allows for the mapping of emissions along the agri‐food value chain using the adapted IO EE approach. Such analysis is valuable in identifying emissions ‘hot‐spots’ along the value chains and analysing potential avenues for emission efficiencies.

Plastics and climate change—Breaking carbon lock-ins through three mitigation pathways

Sustainable battery production with low environmental footprints requires a systematic assessment of the entire value chain, from raw material extraction and processing to battery production and recycling. In order to explore and understand the variations observed in the reported footprints of raw battery materials, it is vital to re-assess the footprints of these material value chains. Identifying the causes of these variations by combining engineering and environmental system analysis expands our knowledge of the footprints of these battery materials. This article disaggregates the value chains of six raw battery materials (aluminum, copper, graphite, lithium carbonate, manganese, and nickel) and identifies the sources of variabilities (levers) for each process along each value chain. We developed a parametric attributional process-based life cycle model to explore the effect of these levers on the greenhouse gas (GHG) emissions of the value chains, expressed in kg of CO2e. The parametric life cycle inventory model is used to conduct distinct life cycle assessments (LCA) for each material value chain by varying the identified levers within defined engineering ranges. 570 distinct LCAs are conducted for the aluminum value chain, 450 for copper, 170 for graphite, 39 for lithium carbonate via spodumene, 20 for lithium carbonate via brine, 260 for manganese, and 440 for nickel. Three-dimensional representations of these results for each value chain in kg of CO2e are presented as contour plots with gradient lines illustrating the intensity of lever combinations on the GHG emissions. The results of this study convey multidimensional insights into how changes in the lever settings of value chains yield variations in the overall GHG emissions of the raw materials. Parameterization of these value chains forms a flexible and high-resolution backbone, leading towards a more reliable life cycle assessment of lithium-ion batteries (LIB).

Toward a circular economy: The role of digitalization

Integrated production of food, energy, fuels and chemicals from rice crops: Multi-objective optimisation for efficient and sustainable value chains

Best available techniques and the value chain perspective

Value chain analysis of the natural gas industry: Lessons from the US regulatory success and opportunities for Europe

Marketing (and sales) is the downstream activity in the value chain as illustrated in Fig. 6.1. Marketing is a continually evolving discipline and as such can be one that companies find themselves left very much behind the competition if they stand still for too long. One example of this evolution has been the fundamental changes to the basic marketing mix. Where once there were 4 Ps to explain the mix, nowadays it is more commonly accepted that more developed 7 Ps add a much-needed additional layer of depth to the marketing mix with some theorists going even further.

Heat transfer bibliography

This article conceptualises the link between firms’ value chains and distribution networks and the requirements for double-materiality assessments in contemporary reporting regulations worldwide. The new European Sustainability Reporting Standards (ESRS) and the standards for sustainability reporting issued by the International Sustainability Standards Board (ISSB), called IFRS S1 and IFRS S2, require companies to report their own direct (scope 1) and indirect (scope 2) greenhouse gas (GHG) emissions as well as GHG emissions in their value chains and distribution networks (both scope 3). However, GHG emissions comprise just one dimension of information that is relevant to understand when assessing, managing and reporting the footprints and impacts of a firm and are, therefore, only a fraction of the key performance indicators (KPIs) related to ESG that should be disclosed. Through a case study, this article demonstrates the connection between a due diligence analysis of a firm’s value chains and distribution networks; an analysis of the competitive parameters of its business model; the identified impacts, risks and opportunities; and the double-materiality perspective. The double-materiality perspective prioritises actions based on probability and significance, creating a natural space to identify KPIs. The implication of this study is that firms can be assisted in identifying relevant KPIs based on double-materiality assessments aided by applying the REGS model because it guides firms in choosing the most relevant KPIs.

This paper analyses the greenhouse gas (GHG) emission along the value chains of two strategic commodities in West Africa (rice and maize) in four pilot countries: Ghana, Senegal Benin and Cote D'Ivoire. The Value Chains Analysis and Greenhouse model used in this study, provides insight into the relationship between output maximization and GHG emissions to help define optimal intervention approaches that minimize emissions while maximizing the potential yield, hence boost food security. It highlights intervention measures for improvement of production and productivity along with the identification of mitigation measures to reduce GHG emissions. It also revealed that the largest GHG emission factor from maize farming in the selected countries is from the application of nitrogen fertilizers (NO2), and for rice farming, depending on the systems, e.g. rain fed, irrigated or multiple aeration, the emission is mostly due to anaerobic decomposition of methane (CH4) which increases with flooding practice.

In situations with imperfect information, the way that value chain actors perceive each other is an important determinant of the value chain's structure and performance. Inaccurate perceptions may result in inefficient value chains, and systematic bias in perceptions may affect inclusiveness. In a case study on perceptions in Ugandan maize supply chains, a random sample of farmers were asked to rate upstream and downstream value chain actors-agro-input dealers, traders, and processors-on a set of important attributes that included ease of access, quality of services rendered, price competitiveness, and overall reputation. These value chain actors were then tracked and asked to assess themselves on the same set of attributes. We find that input dealers, traders, and processors assess themselves more favourably than farmers do. We also focus on heterogeneity in perceptions related to gender and find that for self- assessments, the gender of the value chain actor does not matter. However, the difference between how actors assess themselves and how farmers perceive them is larger for male than for female farmers, as female farmers appear to rate dealers, traders, and processors significantly higher in several dimensions. The gender of the actor being rated does not affect the rating they receive, and gender-based homophily among women is not present in rating behaviour.