Cooperation in manure-based biogas production networks: An agent-based modeling approach

Biogas production from manure has been proposed as a partial solution to energy and environmental concerns. However, manure markets face distortions caused by considerable unbalance between supply and demand and environmental regulations imposed for soil and water protection. Such market distortions influence the cooperation between animal farmers, biogas producers and arable land owners causing fluctuations in manure prices paid (or incurred) by animal farmers. This paper adopts an agent-based modeling approach to investigate the interactions between manure suppliers, i.e., animal farmers, and biogas producers in an industrial symbiosis case example consisting of 19 municipalities in the Overijssel region (eastern Netherlands). To find the manure price for successful cooperation schemes, we measure the impact of manure discharge cost, dimension and dispersion of animal farms, incentives provided by the government for bioenergy production, and the investment costs of biogas plants for different scales on the economic returns for both actor types and favorable market conditions. Findings show that manure exchange prices may vary between −3.33 €/t manure (i.e., animal farmer pays to biogas producer) and 7.03 €/t manure (i.e., biogas producer pays to animal farmer) and thanks to cooperation, actors can create a total economic value added between 3.73 €/t manure and 39.37 €/t manure. Hence, there are cases in which animal farmers can profitably be paid, but the presence of a supply surplus not met by demand provides an advantage to arable land owners and biogas producers in the price contracting phase in the current situation in the Netherlands

Biomass supply contract pricing and environmental policy analysis: A simulation approach

This paper proposes an agent-based simulation model to study the biomass supply contract pricing and policy making in the biofuel industry. In the proposed model, the agents include farmers and a biofuel producer. Farmers\u27 decision-making is assumed to be profit driven, which is formulated as a mixed-integer optimization model, and the biofuel producer\u27s pricing decision is represented with a linear equation with an objective to maximize profits. A case study based on Iowa has been developed to analyze the interactions between the stakeholders and assist determination of the optimal pricing equation for the biofuel producer. Simulation results show that under such a pricing strategy, the biofuel producer can achieve higher profitability than using a fixed price. The impact of government environmental regulations on farmers\u27 decision-making and biomass supply has also been analyzed, and managerial insights have been derived

ASSESSING POTENTIAL ENVIRONMENTAL IMPACTS ACCORDING TO PROBABLE PATTERNS OF SWITCHGRASS ADOPTION IN THE SOUTHEASTERN US

To assess the overall net impact of an emerging technology, life cycle assessment (LCA) must be accompanied by projections of adoption. Diffusion of innovation research provides tools that incorporate economic and social variables to explain and forecast integration of technologies. A switchgrass-to-ethanol case study for the southeastern U.S. is used to demonstrate methods for gauging aggregate environmental effects of an emerging energy technology. Before applying diffusion concepts, breakeven capacities are calculated for land in row crops, hay, pasture and marginal land. Breakeven curves are generated to provide upper bounds to switchgrass adoption over a range of farm-gate prices. The amount and type of land converted to switchgrass provides estimates for the total land use change effects as well as for biomass production and overall impact of the regional switchgrass-to-ethanol system, which is measured by greenhouse gas (GHG) emissions, net fossil energy, and nitrate loss. Maximum switchgrass adoption is assessed within breakeven areas for prices of $50,$100, and $150 per metric ton (Mg). Regression analysis shows that hectares of pasture and hay lands are historically not correlated with crop production profit, which suggests minimal switchgrass adoption. Land in active row crops and marginal land have historically converted land in relation to profitability of crops; consequently, switchgrass is expected to be planted on 75$100 Mg -1 , switchgrass is projected to be grown on about 0.8 million hectares of land in row crops and 0.5 million hectares of the other land categories. This area of production translates to 5.4 billion liters of ethanol, which is about 9% of the gasoline consumed annually in the region. Because land use change (LUC) benefits are enhanced by primarily converting row crops to switchgrass, annual carbon dioxide equivalents of GHG emissions are reduced by about 2 billion kg CO 2 e yr-1 . About 20 years are required to reach such a production level even though national mandates are set for 2022. Including projections of behavior in environmental assessments can inform proactive policy measures that optimize effects of emerging energy technologies

Review of environmental, economic and policy aspects of biofuels

The world is witnessing a sudden growth in production of biofuels, especially those suited for replacing oil like ethanol and biodiesel. This paper synthesizes what the environmental, economic, and policy literature predicts about the possible effects of these types of biofuels. Another motivation is to identify gaps in understanding and recommend areas for future work. The analysis finds three key conclusions. First, the current generation of biofuels, which is derived from food crops, is intensive in land, water, energy, and chemical inputs. Second, the environmental literature is dominated by a discussion of net carbon offset and net energy gain, while indicators relating to impact on human health, soil quality, biodiversity, water depletion, etc., have received much less attention. Third, there is a fast expanding economic and policy literature that analyzes the various effects of biofuels from both micro and macro perspectives, but there are several gaps. A bewildering array of policies - including energy, transportation, agricultural, trade, and environmental policies - is influencing the evolution of biofuels. But the policies and the level of subsidies do not reflect the marginal impact on welfare or the environment. In summary, all biofuels are not created equal. They exhibit considerable spatial and temporal heterogeneity in production. The impact of biofuels will also be heterogeneous, creating winners and losers. The findings of the paper suggest the importance of the role biomass plays in rural areas of developing countries. Furthermore, the use of biomass for producing fuel for cars can affect access to energy and fodder and not just access to food.Energy Production and Transportation,Environmental Economics&Policies,RenewableEnergy,Transport Economics Policy&Planning,Energy and Environment

Regional Scale Biofuel Impact Assessment on Land Use and Carbon Emission - A Case Study for Haryana, India

In the past three decades the world has seen dramatic industrialization and population growth, arousing intense land-use competition. As a result, increasing pressure occurs in both food and energy supply. Bioenergy, especially biofuels that are both renewable clean supplements for non-renewable fossil fuels and also strong competitors of arable land for foodcrops, draw great attention from both sides. In India, biofuel initiatives have gained momentum with the national biofuel policy targeting 20% blending of fossil fuels by 2017 and 27% by 2050. Since India is also involved in fast development and owns the second largest population in the world, there are typical land-use conflicts between food production, biofuels and human settlement. This study, taking the middle-north state of Haryana as an example, aims at estimating the potential to achieve policy targets and its impacts on regional land-use conflicts as well as carbon emission. This report spatially analyses land-use conflicts owing to biofuel expansion. I used an integrated modeling framework to simulate land-use change and biofuel production under two scenarios – food production with/without exportation demand. Under each scenario, three pathways of biofuel production are compared, namely bioethanol from sugarcane molasses, bioethanol from sugarcane bagasse and bioethanol from low-input high-diversity grasses. An empirical model was introduced to measure food demand and human settlement requirements due to population growth. Based on a detailed land-use classification map of Haryana, a social-environmental land-use suitability index across a number of quantitative and qualitative characteristics is constructed for each land-use type in order to define the spatial distribution behaviors. Agricultural behaviors, including carbon emission, impacts on soil organic carbon by irrigation, as well as relations to natural elements such as climate and soil conditions, are simulated by DNDC (DeNitrification-DeComposition) model. An agent-based model is used to investigate how land-use change organized within the region. Each type of land-use is defined as an intelligent agent that is able to interact with surroundings, to choose the optimal position according to land-use suitability index and to make impacts to the environment. This simulation analyzes a period of 40 years from 2010 to 2050 with spatial resolution of 1.28m x 1.28m. Then I analyze annual gaps between biofuels yield and energy target under each scenario.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/109691/1/Haosong_Jiao_practicum_December_2014.pd

Planning the market introduction of new products

In einem zunehmend dynamischen Wettbewerbsumfeld bildet die Fähigkeit zur erfolgreichen Vermarktung von neuen Produkten eine entscheidende Grundlage für den langfristigen Erfolg von Unternehmen. Quantitative Modelle der Verbreitung (Diffusion) von Innovationen in einem sozialen System sind daher sowohl für Wirtschaftswissenschaftler als auch für Manager, die Unterstützung bei der Entwicklung von Markteinführungsstrategien benötigen, von besonderem Interesse. Erste Modelle zur mathematischen Beschreibung von Diffusionsverläufen wurden bereits in den 1960er-Jahren entwickelt. Ziel dieser Modelle ist die empirische Generalisierung von typischen Diffusionsmustern auf aggregierter (d.h. Markt-) Ebene, um den wahrscheinlichen Verlauf der Adoption durch Konsumenten mittels Extrapolation aus frühen Verkaufszahlen abzuschätzen. Für zuverlässige Schätzungen benötigen diese Modelle allerdings Daten über den Großteil des Produktlebenszykluses. Überdies berücksichtigen aggregierte Modelle weder die Heterogenität von Konsumenten noch die Struktur ihrer sozialen Interaktionen. Schließlich eignen sich diese Modelle nur bedingt zur Erprobung des Einflusses von Marketing-Entscheidungsvariablen auf den Diffusionsverlauf. Das Hauptziel dieser Dissertation ist die Entwicklung eines Diffusionsmodells das Entscheidungsträger bei der Planung einer Markteinführungsstrategie für neue Produkte unterstützen kann. Zu diesem Zweck wird agentenbasierte Modellierung und Simulation, eine Methode die in den letzten Jahren in den Sozialwissenschaften zunehmende Verbreitung gefunden hat, eingesetzt. Diese Methode begreift die Diffusion von Innovationen als komplexes emergentes Phänomen, das durch soziale Interaktionen und individuelle Adoptionsentscheidungen von heterogenen Individuen zustande kommt. Ein solcher Bottom-Up-Ansatz ermöglicht es, die prinzipbedingten Einschränkungen von aggregierten Ansätzen zu überwinden und eröffnet damit neue Forschungsmöglichkeiten. Insbesondere können Aspekte wie Adoptionsentscheidungsfaktoren auf Mikroebene, beschränkte Rationalität, unvollständige Information sowie die Heterogenität von Konsumenten hinsichtlich ihrer Präferenzen, ihres Verhaltens und ihrer Verbindungen im sozialen Netzwerk berücksichtigt werden. Die Dissertation zeigt eine Forschungslücke zwischen abstrakten theoretischen Modellen einerseits und angewandten Modellen für bestimmte, sehr spezifische Einsatzbereiche andererseits auf und zielt darauf ab zur Schließung dieser Lücke beizutragen. Der agentenbasierte Ansatz bietet ausgezeichnete Möglichkeiten zur Entwicklung eines generischen und vielseitig einsetzbaren Modells das es erlaubt, aktuelle Forschungsinteressen wie etwa die Diffusion von Innovationen in einem kompetitivem Wettbewerbsumfeld, die räumliche Diffusion von Innovationen oder die Analyse auf Produktebene anstatt auf Branchenebene zu verfolgen. Insbesondere trägt die Dissertation durch (i) die Modellierung aller Stufen des Adoptionsentscheidungsprozesses, (ii) die Erfassung des gesamten Marktes anstatt der Beschränkung auf Erstadoptoren, (iii) die Modellierung der Diffusion einer Innovation in einem Markt mit mehreren Mitbewerbern, (iv) die Erweiterung der zeitlichen Betrachtung von Diffusionsprozessen durch eine räumliche Dimension, (v) die Modellierung eines räumlich definierten sozialen Netzwerkes und (vi) die Einbeziehung von Konsumentenpräferenzen hinsichtlich mehrerer Produktattribute zur Diffusionsforschung bei. Die Eignung des Modells zur Entscheidungsunterstützung in realen Problemstellungen wird anhand eines Anwendungsfalls zur Diffusion eines Biokraftstoffs der zweiten Generation auf dem österreichischen Markt illustriert. Anhand von Simulationsszenarien wird demonstriert, wie das Modell die Planung der Markteinführung einer solchen Innovation unterstützen kann. Ergebnisse zeigen, dass ein wettbewerbsfähiger Preis, wie erwartet, ein wichtiger Adoptionstreiber ist. Zudem weisen die Ergebnisse aber auch darauf hin, dass ein gewisses Marktpotential auch bei einem Preis oberhalb des Niveaus von konventionellen Kraftstoffen besteht. Die Simulation erlaubt potentiellen Investoren die Erprobung unterschiedlicher Strategien zur Auswahl von Vertriebsstellen unter Berücksichtigung von beschränkter Produktionskapazität, lokaler Verfügbarkeit von Rohstoffen und der geographischen Verteilung von Konsumenten. Außerdem ermöglicht es die Simulation, Preisstrategien unter unterschiedlichen Annahmen hinsichtlich zukünftiger Entwicklungen auf dem Rohölmarkt zu testen. Der Anwendungsfall zeigt damit, dass das entwickelte agentenbasierte Diffusionsmodell Entscheidungsträgern wertvolle Unterstützung bei der Entwicklung von Markteinführungsstrategien in einem kompetitiven Marktumfeld bietet.In today’s competitive business environment, firms’ ability to create and maintain competitive advantage and secure long-term survival are critically dependent upon their ability to successful market innovations. Quantitative models of innovation diffusion have therefore attracted strong interest both from management scholars and from practitioners responsible for new product marketing decisions. Pioneering efforts to describe the diffusion of innovations mathematically were made during the 1960s. The aim of these models is to provide empirical generalizations of prototypical diffusion patterns at the aggregate (i.e., market) level in order to estimate the likely diffusion of a new product through extrapolation from early sales; to this end, they typically require considerable amounts of data covering most of the product’s lifespan. Aggregate models cannot account for heterogeneity and social structure and are limited in their potential to evaluate likely effects of decision variables on the diffusion process. The main objective of this thesis is to introduce a diffusion model that can support decision-makers in the process of planning the market introduction of new products. To this end, agent-based modeling and simulation, a methodological innovation that has increasingly been adopted in the social sciences in recent years, is applied to overcome inherent limitations of phenomenological aggregate-level approaches. This bottom-up approach conceives the diffusion of innovations as a complex social phenomenon that emerges from the aggregated individual behavior and the interactions between individuals. It opens up new research opportunities because it can easily incorporate micro-level drivers of adoption, bounded rationality, and imperfect information as well as individuals’ heterogeneity in terms of attributes, preferences, behavior, and linkages in the social network. This thesis identifies and aims at a research gap between purely abstract models of innovation diffusion aimed at general theoretical insights on the one hand, and highly specialized models tailored to a particular practical application on the other hand. The agent-based approach offers excellent opportunities to develop a generic and versatile model that can be applied to a wide range of specific problems. Furthermore, it allows us to pursue cutting-edge research interests including spatial diffusion, diffusion in a competitive context, product-level rather than industry- level analysis, and managerial diagnostics. In particular, the thesis contributes by (i) modeling all stages of the innovation-decision process, (ii) modeling sales rather than exclusively focusing on initial adoption, (iii) modeling the competitive diffusion of multiple products, (iv) complementing the temporal focus with the spatial dimension, (v) incorporating a spatially explicit social network model, and (vi) incorporating multi-attribute consumer decision-making. The capability of the model to tackle real world problems is illustrated by means of a particularly interesting, empirically grounded application study on the diffusion of a second generation biofuel at the Austrian market. Various simulation scenarios demonstrate how the model can be used to plan the market introduction of this innovation. Findings suggest that while a competitive price is unsurprisingly an important driver for adoption, there is a limited market potential for a high quality second generation biofuel at a higher price level than that of conventional fuels. The simulation enables potential investors to assess the effectiveness of various approaches towards selecting gas stations for distribution while accounting for limited production capacity, availability of rich sources of biomass, and the geographic concentration of consumers. It also allows a decision-maker to evaluate the effectiveness of pricing strategies under varying assumptions about future energy market developments. The sample application illustrates how the agent-based model introduced in this thesis can provide managers with valuable decision support in the process of developing product launch strategies in a competitive setting

Modelling of Energy-Crops in Agricultural Sector Models - A Review of Existing Methodologies

The present report provides an overview of the different methodologies applied in partial and general equilibrium models used to analyse biofuel policies in Europe, as well as their methodological pros and cons. While the LEITAP model is included as a general equilibrium model covering biofuel demand, partial equilibrium models are represented by ESIM, FAPRI, AGLINK/COSIMO, RAUMIS, AGMEMOD (agricultural models); POLES and PRIMES (energy sector); and EUFASOM/ENFA (forestry sector). The study is highly relevant for the current modelling work at IPTS, where models such as ESIM and AGLINK play an important role in the Integrated Modelling Platform for Agro-economic Commodity and Policy Analysis (iMAP) of the AGRILIFE Unit. Additionally, the POLES model is currently part of the model portfolio used by the Competitiveness & Sustainability Unit in several studies analysing possible technological pathways of energy production and demand for bioenergy in Europe, a result of implementing the biofuel directive. This compilation of information is also important since the implicit and explicit treatment of bioenergy, either as a demand shock to the processing of oilseeds or feedstock for bioethanol and biodiesel, or as the introduction of a biofuel-sector into a computational general equilibrium (CGE) is foreseen in the short-term by other economic models used at IPTS.JRC.J.5-Agriculture and Life Sciences in the Econom

Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles

Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. Here, we develop state-of-the-art representations of consumer preferences in multiple, global energy- economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to mid-century. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary for driving widespread adoption of clean technologies. Carbon pricing alone is insufficient for bringing low-carbon vehicles to mass market, though it can certainly play a supporting role in ensuring a decarbonised energy supply

Conceptualizing environmental effects of carsharing services: A system thinking approach

Emerging carsharing services and their interconnections with other modes of urban transport, regulations, car manufacturing and population have affected the dynamics of energy consumption, environmental pollution and greenhouse gas emission within a complex system. However, although some aspects of environmental impacts of transport sector have been investigated in the literature, well-deserved studies on the environmental effects of carsharing services following a system thinking approach is missing. This research aims at providing a comprehensive conceptual framework to systematize the interconnections between carsharing services and their environmental effects. To do this, system dynamics (SD) modeling, as a tool to simulate complex and dynamic systems, is applied and the proposed framework model is illustrated by using a causal-loop diagram (CLD). Along with analyzing the main identified causal loops within the presented CLD, relevant strategies are proposed to reduce the negative environmental effects associated with the carsharing services, considering the whole lifecycle of a shared vehicle. The proposed framework can help environment policy makers and shared mobility practitioners in long-term strategic decision-making. Moreover, it can be applied by the researchers as a basis for future research, not only for SD modeling but also other simulation and analysis structures