3,506 research outputs found
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 â3.33 âŹ/t manure (i.e., animal farmer pays to biogas producer) and 7.03 âŹ/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 âŹ/t manure and 39.37 âŹ/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 100, and 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 â 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 FĂ€higkeit zur erfolgreichen
Vermarktung von neuen Produkten eine entscheidende Grundlage fĂŒr den langfristigen Erfolg
von Unternehmen.
Quantitative Modelle der Verbreitung (Diffusion) von Innovationen in einem sozialen System
sind daher sowohl fĂŒr Wirtschaftswissenschaftler als auch fĂŒr Manager, die UnterstĂŒtzung bei
der Entwicklung von MarkteinfĂŒhrungsstrategien benötigen, von besonderem Interesse.
Erste Modelle zur mathematischen Beschreibung von DiffusionsverlÀufen 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 frĂŒhen Verkaufszahlen abzuschĂ€tzen.
FĂŒr zuverlĂ€ssige SchĂ€tzungen benötigen diese Modelle allerdings
Daten ĂŒber den GroĂteil des Produktlebenszykluses.
Ăberdies berĂŒcksichtigen aggregierte Modelle weder die HeterogenitĂ€t von Konsumenten
noch die Struktur ihrer sozialen Interaktionen. SchlieĂlich 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 EntscheidungstrĂ€ger bei der Planung einer MarkteinfĂŒhrungsstrategie fĂŒr
neue Produkte unterstĂŒtzen 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 PhÀnomen, das durch soziale Interaktionen und individuelle
Adoptionsentscheidungen von heterogenen Individuen zustande kommt.
Ein solcher Bottom-Up-Ansatz ermöglicht es, die prinzipbedingten EinschrÀnkungen
von aggregierten AnsĂ€tzen zu ĂŒberwinden und eröffnet damit neue Forschungsmöglichkeiten.
Insbesondere können Aspekte wie Adoptionsentscheidungsfaktoren auf Mikroebene, beschrÀnkte
RationalitÀt, unvollstÀndige Information sowie die HeterogenitÀt von Konsumenten hinsichtlich
ihrer PrÀferenzen, ihres Verhaltens und ihrer Verbindungen im sozialen Netzwerk
berĂŒcksichtigt werden.
Die Dissertation zeigt eine ForschungslĂŒcke zwischen abstrakten theoretischen
Modellen einerseits und angewandten Modellen fĂŒr bestimmte, sehr spezifische
Einsatzbereiche andererseits auf und zielt darauf ab zur SchlieĂung dieser LĂŒcke 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 rÀumliche Diffusion von Innovationen oder die Analyse auf Produktebene
anstatt auf Branchenebene zu verfolgen.
Insbesondere trÀgt die Dissertation durch
(i) die Modellierung aller Stufen des Adoptionsentscheidungsprozesses,
(ii) die Erfassung des gesamten Marktes anstatt der BeschrÀnkung 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 rÀumliche Dimension,
(v) die Modellierung eines rÀumlich definierten sozialen Netzwerkes und
(vi) die Einbeziehung von KonsumentenprÀferenzen hinsichtlich mehrerer Produktattribute
zur Diffusionsforschung bei.
Die Eignung des Modells zur EntscheidungsunterstĂŒtzung 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
MarkteinfĂŒhrung einer solchen Innovation unterstĂŒtzen kann.
Ergebnisse zeigen, dass ein wettbewerbsfÀhiger 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 BerĂŒcksichtigung von beschrĂ€nkter
ProduktionskapazitĂ€t, lokaler VerfĂŒgbarkeit von Rohstoffen und der
geographischen Verteilung von Konsumenten. AuĂerdem ermöglicht es die Simulation,
Preisstrategien unter unterschiedlichen Annahmen hinsichtlich zukĂŒnftiger Entwicklungen
auf dem Rohölmarkt zu testen.
Der Anwendungsfall zeigt damit, dass das entwickelte agentenbasierte Diffusionsmodell
EntscheidungstrĂ€gern wertvolle UnterstĂŒtzung bei der Entwicklung von MarkteinfĂŒhrungsstrategien
in einem kompetitiven Marktumfeld bietet.In todayâs competitive business environment, firmsâ 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 productâs 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
individualsâ 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
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